Above are papers related to the following paper: [Paper_ID] Paper #54129. 	[Title] Strongly Typed Genetic Programming [Abstract] BBN Technical Report #7866: Abstract Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of the same data type. To correct this deficiency, we introduce a variation of genetic programming called "strongly typed" genetic programming (STGP). In STGP, variables, constants, arguments, and returned values can be of any data type with the provision that the data type for each such value be specified beforehand. This allows the initialization process and the genetic operators to only generate syntactically correct parse trees. Key concepts for STGP are generic functions, which are not true strongly typed functions but rather templates for classes of such functions, and generic data types, which are analogous. To illustrate STGP, we present four examples involving vector/matrix manipulation and list manipulation: (1) the multi-dimensional least-squares regression problem, (2) the multi-dimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128453. 	[Title] An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization [Abstract] In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127430. 	[Title] A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3CNF Problems [Abstract] Genetic algorithms have been used to solve hard optimization problems ranging from the Travelling Salesman problem to the Quadratic Assignment problem. We show that the Simple Genetic Algorithm can be used to solve an optimization problem derived from the 3-Conjunctive Normal Form problem. By separating the populations into small sub-populations, parallel genetic algorithms exploits the inherent parallelism in genetic algorithms and prevents premature convergence. Genetic algorithms using hill-climbing conduct genetic search in the space of local optima, and hill-climbing can be less com-putationally expensive than genetic search. We examine the effectiveness of these techniques in improving the quality of solutions of 3CNF problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1033. 	[Title] The Evolutionary Cost of Learning [Abstract] Traits that are acquired by members of an evolving population during their lifetime, through adaptive processes such as learning, can become genetically specified in later generations. Thus there is a change in the level of learning in the population over evolutionary time. This paper explores the idea that as well as the benefits to be gained from learning, there may also be costs to be paid for the ability to learn. It is these costs that supply the selection pressure for the genetic assimilation of acquired traits. Two models are presented that attempt to illustrate this assertion. The first uses Kauffman's NK fitness landscapes to show the effect that both explicit and implicit costs have on the assimilation of learnt traits. A characteristic `hump' is observed in the graph of the level of plasticity in the population showing that learning is first selected for and then against as evolution progresses. The second model is a practical example in which neural network controllers are evolved for a small mobile robot. Results from this experiment also show the hump. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125386. 	[Title] 21 Using n 2 classifier in constructive induction [Abstract] In this paper, we propose a multi-classification approach for constructive induction. The idea of an improvement of classification accuracy is based on iterative modification of input data space. This process is independently repeated for each pair of n classes. Finally, it gives (n 2 n)/2 input data subspaces of attributes dedicated for optimal discrimination of appropriate pairs of classes. We use genetic algorithms as a constructive induction engine. A final classification is obtained by a weighted majority voting rule, according to n 2 - classifier approach. The computational experiment was performed on medical data set. The obtained results point out the advantage of using a multi-classification model (n 2 classifier) in constructive induction in relation to the analogous single-classifier approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #108047. 	[Title] Evolving a Team [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128985. 	[Title] Evolving Visual Routines  Architecture and Planning, [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #128540. 	[Title] Adapting Control Strategies for Situated Autonomous Agents [Abstract] This paper studies how to balance evolutionary design and human expertise in order to best design situated autonomous agents which can learn specific tasks. A genetic algorithm designs control circuits to learn simple behaviors, and given control strategies for simple behaviors, the genetic algorithm designs a combinational circuit that switches between these simple behaviors to perform a navigation task. Keywords: Genetic Algorithms, Computational Design, Autonomous Agents, Robotics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #141342. 	[Title] Dynamic Parameter Encoding for Genetic Algorithms [Abstract] The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #206371. 	[Title] Genetic Algorithms for Adaptive Planning of Path and Trajectory of a Mobile Robot in 2D Terrains [Abstract] This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss extensions of the GA for solving both path planning and trajectory planning simultaneously. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #141347. 	[Title] Adapting Crossover in a Genetic Algorithm [Abstract] Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Despite theoretical analysis, however, it appears difficult to predict when a particular crossover form will be optimal for a given problem. This paper describes an adaptive genetic algorithm that decides, as it runs, which form is optimal. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131557. 	[Title] Specialization in Populations of Artificial Neural Networks [Abstract] Specialization in populations of artificial neural networks is studied. Organisms with both fixed and evolvable fitness formulae are placed in isolated and shared environments, and the emerged behaviors are compared. An evolvable fitness formula specifies, that the evaluation measure is let free to evolve, and we obtain co-evolution of the expressed behavior and the individual evolvable fitness formula. In an isolated environment a generalist behavior emerges when organisms have a fixed fitness formula, and a specialist behavior emerges when organisms have individual evolvable fitness formulae. A population diversification analysis shows, that almost all organisms in a population in an isolated environment converge towards the same behavioral strategy, while we find, that competition can act to provide population diversification in populations of organisms in a shared environment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #178727. 	[Title] AN APPROACH TO A PROBLEM IN NETWORK DESIGN USING GENETIC ALGORITHMS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153577. 	[Title]  [Abstract] Eugenic Evolution for Combinatorial Optimization John William Prior Report AI98-268 May 1998 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153065. 	[Title]  [Abstract] CBR Assisted Explanation of GA Results Computer Science Technical Report number 361 CRCC Technical Report number 63 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1050679. 	[Title] Diplomarbeit A Genetic Algorithm for the Topological Optimization of Neural Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103482. 	[Title] Trading Spaces: Computation, Representation and the Limits of Uninformed Learning [Abstract] fl Research on this paper was partly supported by a Senior Research Leave fellowship granted by the Joint Council (SERC/MRC/ESRC) Cognitive Science Human Computer Interaction Initiative to one of the authors (Clark). Thanks to the Initiative for that support. y The order of names is arbitrary. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129018. 	[Title] Signal Path Oriented Approach for Generation of Dynamic Process Models [Abstract] The article at hand discusses a tool for automatic generation of structured models for complex dynamic processes by means of genetic programming. In contrast to other techniques which use genetic programming to find an appropriate arithmetic expression in order to describe the input-output behaviour of a process, this tool is based on a block oriented approach with a transparent description of signal paths. A short survey on other techniques for computer based system identification is given and the basic concept of SMOG (Structured MOdel Generator) is described. Furthermore latest extensions of the system are presented in detail, including automatically defined sub-models and quali tative fitness criteria. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129027. 	[Title] Genetic Programming Methodology, Parallelization and Applications  par [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #117316. 	[Title] A Simulation of Adaptive Agents in a Hostile Environment [Abstract] In this paper we use the genetic programming technique to evolve programs to control an autonomous agent capable of learning how to survive in a hostile environment. In order to facilitate this goal, agents are run through random environment configurations. Randomly generated programs, which control the interaction of the agent with its environment, are recombined to form better programs. Each generation of the population of agents is placed into the Simulator with the ultimate goal of producing an agent capable of surviving any environment. The environment that an agent is presented consists of other agents, mines, and energy. The goal of this research is to construct a program which when executed will allow an agent (or agents) to correctly sense, and mark, the presence of items (energy and mines) in any environment. The Simulator determines the raw fitness of each agent by interpreting the associated program. General programs are evolved to solve this problem. Different environmental setups are presented to show the generality of the solution. These environments include one agent in a fixed environment, one agent in a fluctuating environment, and multiple agents in a fluctuating environment cooperating together. The genetic programming technique was extremely successful. The average fitness per generation in all three environments tested showed steady improvement. Programs were successfully generated that enabled an agent to handle any possible environment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17477. 	[Title] Co-Evolving Soccer Softbot Team Coordination with Genetic Programming [Abstract] Genetic Programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast to other learning methods, Genetic Programming's automatic programming makes it a natural approach for developing algorithmic robot behaviors. In this paper we present an overview of how we apply Genetic Programming to behavior-based team coordination in the RoboCup Soccer Server domain. The result is not just a hand-coded soccer algorithm, but a team of softbots which have learned on their own how to play a reasonable game of soccer. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #117315. 	[Title] Evolving Cooperation Strategies [Abstract] The identification, design, and implementation of strategies for cooperation is a central research issue in the field of Distributed Artificial Intelligence (DAI). We propose a novel approach to the construction of cooperation strategies for a group of problem solvers based on the Genetic Programming (GP) paradigm. GPs are a class of adaptive algorithms used to evolve solution structures that optimize a given evaluation criterion. Our approach is based on designing a representation for cooperation strategies that can be manipulated by GPs. We present results from experiments in the predator-prey domain, which has been extensively studied as a easy-to-describe but difficult-to-solve cooperation problem domain. The key aspect of our approach is the minimal reliance on domain knowledge and human intervention in the construction of good cooperation strategies. Promising comparison results with prior systems lend credence to the viability of this ap proach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #249421. 	[Title] PERCEPTION OF TIME AS PHASE: TOWARD AN ADAPTIVE-OSCILLATOR MODEL OF RHYTHMIC PATTERN PROCESSING 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578645. 	[Title] Entailment for Specification Refinement [Abstract] Specification refinement is part of formal program derivation, a method by which software is directly constructed from a provably correct specification. Because program derivation is an intensive manual exercise used for critical software systems, an automated approach would allow it to be viable for many other types of software systems. The goal of this research is to determine if genetic programming (GP) can be used to automate the specification refinement process. The initial steps toward this goal are to show that a well-known proof logic for program derivation can be encoded such that a GP-based system can infer sentences in the logic for proof of a particular sentence. The results are promising and indicate that GP can be useful in aiding pro gram derivation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578646. 	[Title] Type Inheritance in Strongly Typed Genetic Programming [Abstract] This paper appears as chapter 18 of Kenneth E. Kinnear, Jr. and Peter J. Angeline, editors Advances in Genetic Programming 2, MIT Press, 1996. Abstract Genetic Programming (GP) is an automatic method for generating computer programs, which are stored as data structures and manipulated to evolve better programs. An extension restricting the search space is Strongly Typed Genetic Programming (STGP), which has, as a basic premise, the removal of closure by typing both the arguments and return values of functions, and by also typing the terminal set. A restriction of STGP is that there are only two levels of typing. We extend STGP by allowing a type hierarchy, which allows more than two levels of typing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103960. 	[Title] Surgery [Abstract] Object localization has applications in many areas of engineering and science. The goal is to spatially locate an arbitrarily-shaped object. In many applications, it is desirable to minimize the number of measurements collected for this purpose, while ensuring sufficient localization accuracy. In surgery, for example, collecting a large number of localization measurements may either extend the time required to perform a surgical procedure, or increase the radiation dosage to which a patient is exposed. Localization accuracy is a function of the spatial distribution of discrete measurements over an object when measurement noise is present. In [Simon et al., 1995a], metrics were presented to evaluate the information available from a set of discrete object measurements. In this study, new approaches to the discrete point data selection problem are described. These include hillclimbing, genetic algorithms (GAs), and Population-Based Incremental Learning (PBIL). Extensions of the standard GA and PBIL methods, which employ multiple parallel populations, are explored. The results of extensive empirical testing are provided. The results suggest that a combination of PBIL and hillclimbing result in the best overall performance. A computer-assisted surgical system which incorporates some of the methods presented in this paper is currently being evaluated in cadaver trials. Evolution-Based Methods for Selecting Point Data Shumeet Baluja was supported by a National Science Foundation Graduate Student Fellowship and a Graduate Student Fellowship from the National Aeronautics and Space Administration, administered by the Lyndon B. Johnson Space Center, Houston, TX. David Simon was partially supported by a National Science Foundation National Challenge grant (award IRI-9422734). for Object Localization: Applications to Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578649. 	[Title] Augmenting Collective Adaptation with Simple Process Agents [Abstract] We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. However, there is still considerable scope for improvement. In collective adaptation, search agents gather knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting the exploration of the search agents. We examine the utility of increasing the capabilities of the centralized process agents. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103515. 	[Title] The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance [Abstract] Genetic algorithms (GAs) play a major role in many artificial-life systems, but there is often little detailed understanding of why the GA performs as it does, and little theoretical basis on which to characterize the types of fitness landscapes that lead to successful GA performance. In this paper we propose a strategy for addressing these issues. Our strategy consists of defining a set of features of fitness landscapes that are particularly relevant to the GA, and experimentally studying how various configurations of these features affect the GA's performance along a number of dimensions. In this paper we informally describe an initial set of proposed feature classes, describe in detail one such class ("Royal Road" functions), and present some initial experimental results concerning the role of crossover and "building blocks" on landscapes constructed from features of this class. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129573. 	[Title] The Exploitation of Cooperation in Iterated Prisoner's Dilemma [Abstract] We follow Axelrod [2] in using the genetic algorithm to play Iterated Prisoner's Dilemma. Each member of the population (i.e., each strategy) is evaluated by how it performs against the other members of the current population. This creates a dynamic environment in which the algorithm is optimising to a moving target instead of the usual evaluation against some fixed set of strategies, causing an "arms race" of innovation [3]. We conduct two sets of experiments. The first set investigates what conditions evolve the best strategies. The second set studies the robustness of the strategies thus evolved, that is, are the strategies useful only in the round robin of its population or are they effective against a wide variety of opponents? Our results indicate that the population has nearly always converged by about 250 generations, by which time the bias in the population has almost always stabilised at 85%. Our results confirm that cooperation almost always becomes the dominant strategy [1, 2]. We can also confirm that seeding the population with expert strategies is best done in small amounts so as to leave the initial population with plenty of genetic diversity [7]. The lack of robustness in strategies produced in the round robin evaluation is demonstrated by some examples of a population of nave cooperators being exploited by a defect-first strategy. This causes a sudden but ephemeral decline in the population's average score, but it recovers when less nave cooperators emerge and do well against the exploiting strategies. This example of runaway evolution is brought back to reality by a suitable mutation, reminiscent of punctuated equilibria [12]. We find that a way to reduce such navity is to make the GA population play against an extra, Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #248425. 	[Title] A Comparison of Selection Schemes used in Genetic Algorithms [Abstract] TIK-Report Nr. 11, December 1995 Version 2 (2. Edition) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131116. 	[Title] 3 Representation Issues in Neighborhood Search and Evolutionary Algorithms [Abstract] Evolutionary Algorithms are often presented as general purpose search methods. Yet, we also know that no search method is better than another over all possible problems and that in fact there is often a good deal of problem specific information involved in the choice of problem representation and search operators. In this paper we explore some very general properties of representations as they relate to neighborhood search methods. In particular, we looked at the expected number of local optima under a neighborhood search operator when averaged overall possible representations. The number of local optima under a neighborhood search operator for standard Binary and standard binary reflected Gray codes is developed and explored as one measure of problem complexity. We also relate number of local optima to another metric, , designed to provide one measure of complexity with respect to a simple genetic algorithm. Choosing a good representation is a vital component of solving any search problem. However, choosing a good representation for a problem is as difficult as choosing a good search algorithm for a problem. Wolpert and Macready's (1995) No Free Lunch (NFL) theorem proves that no search algorithm is better than any other over all possible discrete functions. Radcliffe and Surry (1995) extend these notions to also cover the idea that all representations are equivalent when their behavior is considered on average over all possible functions. To understand these results, we first outline some of the simple assumptions behind this theorem. First, assume the optimization problem is discrete; this describes all combinatorial optimization problems-and really all optimization problems being solved on computers since computers have finite precision. Second, we ignore the fact that we can resample points in the space. The "No Free Lunch" result can be stated as follows: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594543. 	[Title] A Methodology for Processing Problem Constraints in Genetic Programming [Abstract] Search mechanisms of artificial intelligence combine two elements: representation, which determines the search space, and a search mechanism, which actually explores the space. Unfortunately, many searches may explore redundant and/or invalid solutions. Genetic programming refers to a class of evolutionary algorithms based on genetic algorithms but utilizing a parameterized representation in the form of trees. These algorithms perform searches based on simulation of nature. They face the same problems of redundant/invalid subspaces. These problems have just recently been addressed in a systematic manner. This paper presents a methodology devised for the public domain genetic programming tool lil-gp. This methodology uses data typing and semantic information to constrain the representation space so that only valid, and possibly unique, solutions will be explored. The user enters problem-specific constraints, which are transformed into a normal set. This set is checked for feasibility, and subsequently it is used to limit the space being explored. The constraints can determine valid, possibly unique space. Moreover, they can also be used to exclude subspaces the user considers uninteresting, using some problem-specific knowledge. A simple example is followed thoroughly to illustrate the constraint language, transformations, and the normal set. Experiments with boolean 11-multiplexer illustrate practical applications of the method to limit redundant space exploration by utilizing problem-specific knowledge. fl Supported by a grant from NASA/JSC: NAG 9-847. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #263279. 	[Title] Data Analyses Using Simulated Breeding and Inductive Learning Methods [Abstract] Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor pre-determined evaluation criteria. The problem is how domain experts get simple, easy-to-understand, and accurate knowledge from noisy data. This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are easy to understand; and they are at the same level on the accuracy compared with the other methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103985. 	[Title] HGA: A Hardware-Based Genetic Algorithm [Abstract] A genetic algorithm (GA) is a robust problem-solving method based on natural selection. Hardware's speed advantage and its ability to parallelize offer great rewards to genetic al gorithms. Speedups of 1-3 orders of magnitude have been observed when frequently used software routines were im plemented in hardware by way of reprogrammable field-pro grammable gate arrays (FPGAs). Reprogrammability is es sential in a general-purpose GA engine because certain GA modules require changeability (e.g. the function to be opti mized by the GA). Thus a hardware-based GA is both feasi ble and desirable. A fully functional hardware-based genetic algorithm (the HGA) is presented here as a proof-of-concept system. It was designed using VHDL to allow for easy scala bility. It is designed to act as a coprocessor with the CPU of a PC. The user programs the FPGAs which implement the function to be optimized. Other GA parameters may also be specified by the user. Simulation results and performance analyses of the HGA are presented. A prototype HGA is de scribed and compared to a similar GA implemented in soft ware. In the simple tests, the prototype took about 6% as many clock cycles to run as the software-based GA. Further suggested improvements could realistically make the HGA 2-3 orders of magnitude faster than the software-based GA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #259701. 	[Title] Modeling Simple Genetic Algorithms for Permutation Problems [Abstract] An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all permutations in the search space. The mixing matrices for various permutation based operators are also developed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #259702. 	[Title] Evaluating Evolutionary Algorithms [Abstract] Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly defined evaluation goals. In this paper we discuss some basic principles that can be used to develop test suites and we examine the role of test suites as they have been used to evaluate evolutionary search algorithms. Current test suites include functions that are easily solved by simple search methods such as greedy hill-climbers. Some test functions also have undesirable characteristics that are exaggerated as the dimensionality of the search space is increased. New methods are examined for constructing functions with different degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of the search space. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593022. 	[Title] 1 GP-COM: A Distributed, Component-Based Genetic Programming System in C++ [Abstract] Widespread adoption of Genetic Programming techniques as a domain-independent problem solving tool depends on a good underlying software structure. A system is presented that mirrors the conceptual makeup of a GP system. Consisting of a loose collection of software components, each with strict interface definitions and roles, the system maximises flexibility and minimises effort when applied to a new problem domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154176. 	[Title] operations: operation machine duration [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133196. 	[Title] Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm [Abstract] This paper examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments provide preliminary evidence that asynchronous versions of these algorithms have a lower run time than synchronous GAs. Our analysis shows that this improvement is due to (1) decreased synchronization costs and (2) high numerical efficiency (e.g. fewer function evaluations) for the asynchronous GAs. This analysis includes a critique of the utility of traditional parallel performance measures for parallel GAs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18582. 	[Title] Knowledge-Based Genetic Learning [Abstract] Genetic algorithms have been proven to be a powerful tool within the area of machine learning. However, there are some classes of problems where they seem to be scarcely applicable, e.g. when the solution to a given problem consists of several parts that influence each other. In that case the classic genetic operators cross-over and mutation do not work very well thus preventing a good performance. This paper describes an approach to overcome this problem by using high-level genetic operators and integrating task specific but domain independent knowledge to guide the use of these operators. The advantages of this approach are shown for learning a rule base to adapt the parameters of an image processing operator path within the SOLUTION system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136791. 	[Title] Program Optimization for Faster Genetic Programming [Abstract] We have used genetic programming to develop efficient image processing software. The ultimate goal of our work is to detect certain signs of breast cancer that cannot be detected with current segmentation and classification methods. Traditional techniques do a relatively good job of segmenting and classifying small-scale features of mammo-grams, such as micro-calcification clusters. Our strongly-typed genetic programs work on a multi-resolution representation of the mammogram, and they are aimed at handling features at medium and large scales, such as stel-lated lesions and architectural distortions. The main problem is efficiency. We employ program optimizations that speed up the evolution process by more than a factor of ten. In this paper we present our genetic programming system, and we describe our optimization techniques. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1688. 	[Title] Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems [Abstract] Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NP-Complete, any other NP-Complete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either paradigm. This technique is illustrated for hamiltonian circuit (HC) problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136814. 	[Title] A Comparative Study of Genetic Search [Abstract] We present a comparative study of genetic algorithms and their search properties when treated as a combinatorial optimization technique. This is done in the context of the NP-hard problem MAX-SAT, the comparison being relative to the Metropolis process, and by extension, simulated annealing. Our contribution is two-fold. First, we show that for large and difficult MAX-SAT instances, the contribution of cross-over to the search process is marginal. Little is lost if it is dispensed altogether, running mutation and selection as an enlarged Metropolis process. Second, we show that for these problem instances, genetic search consistently performs worse than simulated annealing when subject to similar resource bounds. The correspondence between the two algorithms is made more precise via a decomposition argument, and provides a framework for interpreting our results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120431. 	[Title] Symbolic and Subsymbolic Learning for Vision: Some Possibilities [Abstract] Robust, flexible and sufficiently general vision systems such as those for recognition and description of complex 3-dimensional objects require an adequate armamentarium of representations and learning mechanisms. This paper briefly analyzes the strengths and weaknesses of different learning paradigms such as symbol processing systems, connectionist networks, and statistical and syntactic pattern recognition systems as possible candidates for providing such capabilities and points out several promising directions for integrating multiple such paradigms in a synergistic fashion towards that goal. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28336. 	[Title] Evolving Behavioral Strategies in Predators and Prey [Abstract] The predator/prey domain is utilized to conduct research in Distributed Artificial Intelligence. Genetic Programming is used to evolve behavioral strategies for the predator agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #175291. 	[Title] Actively Searching for an Effective Neural-Network Ensemble [Abstract] A neural-network ensemble is a very successful technique where the outputs of a set of separately trained neural network are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm called Addemup that uses genetic algorithms to explicitly search for a highly diverse set of accurate trained networks. Addemup works by first creating an initial population, then uses genetic operators to continually create new networks, keeping the set of networks that are highly accurate while disagreeing with each other as much as possible. Experiments on four real-world domains show that Addemup is able to generate a set of trained networks that is more accurate than several existing ensemble approaches. Experiments also show that Addemup is able to effectively incorporate prior knowledge, if available, to improve the quality of its ensemble. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #132806. 	[Title] USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR [Abstract] A new mechanism for genetic encoding of neural networks is proposed, which is loosely based on the marker structure of biological DNA. The mechanism allows all aspects of the network structure, including the number of nodes and their connectivity, to be evolved through genetic algorithms. The effectiveness of the encoding scheme is demonstrated in an object recognition task that requires artificial creatures (whose behaviour is driven by a neural network) to develop high-level finite-state exploration and discrimination strategies. The task requires solving the sensory-motor grounding problem, i.e. developing a functional understanding of the effects that a creature's movement has on its sensory input. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #148170. 	[Title] Learning Monitoring Strategies: A Difficult Genetic Programming Application [Abstract] Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviors. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1109199. 	[Title] Monitoring in Embedded Agents [Abstract] Finding good monitoring strategies is an important process in the design of any embedded agent. We describe the nature of the monitoring problem, point out what makes it difficult, and show that while periodic monitoring strategies are often the easiest to derive, they are not always the most appropriate. We demonstrate mathematically and empirically that for a wide class of problems, the so-called "cupcake problems", there exists a simple strategy, interval reduction, that outperforms periodic monitoring. We also show how features of the environment may influence the choice of the optimal strategy. The paper concludes with some thoughts about a monitoring strategy taxonomy, and what its defining features might be. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129683. 	[Title] A Stochastic Search Approach to Grammar Induction [Abstract] This paper describes a new sampling-based heuristic for tree search named SAGE and presents an analysis of its performance on the problem of grammar induction. This last work has been inspired by the Abbadingo DFA learning competition [14] which took place between Mars and November 1997. SAGE ended up as one of the two winners in that competition. The second winning algorithm, first proposed by Rod-ney Price, implements a new evidence-driven heuristic for state merging. Our own version of this heuristic is also described in this paper and compared to SAGE. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114331. 	[Title] Island Model Genetic Algorithms and Linearly Separable Problems [Abstract] Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. On the other hand, linearly separable functions have often been used to test Island Model Genetic Algorithms; it is possible that Island Models are particular well suited to separable problems. We look at how Island Models can track multiple search trajectories using the infinite population models of the simple genetic algorithm. We also introduce a simple model for better understanding when Island Model Genetic Algorithms may have an advantage when processing linearly separable problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578780. 	[Title] Towards Automatic Discovery of Building Blocks in Genetic Programming [Abstract] This paper presents an algorithm for the discovery of building blocks in genetic programming (GP) called adaptive representation through learning (ARL). The central idea of ARL is the adaptation of the problem representation, by extending the set of terminals and functions with a set of evolvable subroutines. The set of subroutines extracts common knowledge emerging during the evolutionary process and acquires the necessary structure for solving the problem. ARL supports subroutine creation and deletion. Subroutine creation or discovery is performed automatically based on the differential parent-offspring fitness and block activation. Subroutine deletion relies on a utility measure similar to schema fitness over a window of past generations. The technique described is tested on the problem of controlling an agent in a dynamic and non-deterministic environment. The automatic discovery of subroutines can help scale up the GP technique to complex problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113831. 	[Title] Determining Successful Negotiation Strategies: An Evolutionary Approach [Abstract] To be successful in open, multi-agent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the relative success of different strategies against different types of opponent in different environments. In particular, we adopt an evolutionary approach in which strategies and tactics correspond to the genetic material in a genetic algorithm. We conduct a series of experiments to determine the most successful strategies and to see how and when these strategies evolve depending on the context and negotiation stance of the agent's opponent. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131752. 	[Title] Tracing the Behavior of Genetic Algorithms Using Expected Values of Bit and Walsh Products [Abstract] We consider two methods for tracing genetic algorithms. The first method is based on the expected values of bit products and the second method on the expected values of Walsh products. We treat proportional selection, mutation and uniform and one-point crossover. As applications, we obtain results on stable points and fitness of schemata. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #190697. 	[Title] Analysis of Neurocontrollers Designed by Simulated Evolution [Abstract] Randomized, adaptive, greedy search using evolutionary algorithms offers a powerful and versatile approach to the automated design of neural network architectures for a variety of tasks in artificial intelligence and robotics. In this paper we present results from the evolutionary design of a neuro-controller for a robotic bulldozer. This robot is given the task of clearing an arena littered with boxes by pushing boxes to the sides. Through a careful analysis of the evolved networks we show how evolution exploits the design constraints and properties of the environment to produce network structures of high fitness. We conclude with a brief summary of related ongoing research examining the intricate interplay between environment and evolutionary processes in determining the structure and function of the resulting neural architectures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #190706. 	[Title] Selection for Wandering Behavior in a Small Robot [Abstract] We have evolved artificial neural networks to control the wandering behavior of small robots. The task was to touch as many squares in a grid as possible during a fixed period of time. A number of the simulated robots were embodied in small Lego (Trademark) robot, controlled by a Motorola (Trademark) 6811 processor; and their performance was compared to the simulations. We observed that: (a) evolution was an effective means to program control; (b) progress was characterized by sharply stepped periods of improvement, separated by periods of stasis that corresponded to levels of behavioral/computational complexity; and (c) the simulated and realized robots behaved quite similarly, the realized robots in some cases outperforming the simulated ones. Introducing random noise to the simulations improved the fit somewhat (from 0.73 to 0.79). Hybrid simulated/embodied selection regimes for evolutionary robots are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #273152. 	[Title] Evolutionary Module Acquisition [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153280. 	[Title]  [Abstract] A Genetic Algorithm Tutorial Darrell Whitley Technical Report CS-93-103 (Revised) November 10, 1993 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #229635. 	[Title] Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems [Abstract] Technical Report AI94-218 May 1994 Abstract A new method for developing good value-ordering strategies in constraint satisfaction search is presented. Using an evolutionary technique called SANE, in which individual neurons evolve to cooperate and form a neural network, problem-specific knowledge can be discovered that results in better value-ordering decisions than those based on problem-general heuristics. A neural network was evolved in a chronological backtrack search to decide the ordering of cars in a resource-limited assembly line. The network required 1/30 of the backtracks of random ordering and 1/3 of the backtracks of the maximization of future options heuristic. The SANE approach should extend well to other domains where heuristic information is either difficult to discover or problem-specific. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128204. 	[Title] The Application of a Parallel Genetic Algorithm to the n=m=P=C max Flowshop Problem [Abstract] Hard combinatorial problems in sequencing and scheduling led recently into further research of genetic algorithms. Canonical coding of the symmetric TSP can be modified into a coding of the n-job m-machine flowshop problem, which configurates the solution space in a different way. We show that well known genetic operators act intelligently on this coding scheme. They implecitely prefer a subset of solutions which contain the probably best solutions with respect to an objective. We conjecture that every new problem needs a determination of this necessary condition for a genetic algorithm to work, i. e. a proof by experiment. We implemented an asynchronous parallel genetic algorithm on a UNIX-based computer network. Computational results of the new heuristic are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119505. 	[Title] A Preliminary Investigation of Evolution as a Form Design Strategy [Abstract] We describe the preliminary version of our investigative software, GGE Generative Genetic Explorer, in which genetic operations interact with Au-toCAD to generate novel 3D forms for the architect. GGE allows us to asess how evolutionary algorithms should be tailored to suit Architecture CAD tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133338. 	[Title] Vector Quantizer Design Using Genetic Algorithms [Abstract] A Genetic Algorithmic (GA) approach to vector quantizer design that combines the conventional Generalized Lloyd Algorithm (GLA) [6] is presented. We refer to this hybrid as the Genetic Generalized Lloyd Algorithm (GGLA). It works briefly as follows: A finite number of codebooks, called chromosomes, are selected. Each codebook undergoes iterative cycles of reproduction. We perform experiments with various alternative design choices using Gaussian-Markov processes, speech, and image as source data and signal-to-noise ratio (SNR) as the performance measure. In most cases, the GGLA showed performance improvements with respect to the GLA. We also compare our results with the Zador-Gersho formula [2, 9]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12576. 	[Title] Learning Sequential Decision Rules Using Simulation Models and Competition [Abstract] The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128227. 	[Title] On the Effectiveness of Evolutionary Search in High-Dimensional NK-Landscapes [Abstract] NK-landscapes offer the ability to assess the performance of evolutionary algorithms on problems with different degrees of epistasis. In this paper, we study the performance of six algorithms in NK-landscapes with low and high dimension while keeping the amount of epistatic interactions constant. The results show that compared to genetic local search algorithms, the performance of standard genetic algorithms employing crossover or mutation significantly decreases with increasing problem size. Furthermore, with increasing K, crossover based algorithms are in both cases outperformed by mutation based algorithms. However, the relative performance differences between the algorithms grow significantly with the dimension of the search space, indicating that it is important to consider high-dimensional landscapes for evaluating the performance of evolutionary algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1117476. 	[Title] Hierarchical Self-Organization in Genetic Programming [Abstract] This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation on-the-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibility of approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem's fitness function. Preliminary empirical results are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198443. 	[Title] Properties of Genetic Representations of Neural Architectures [Abstract] Genetic algorithms and related evolutionary techniques offer a promising approach for automatically exploring the design space of neural architectures for artificial intelligence and cognitive modeling. Central to this process of evolutionary design of neural architectures (EDNA) is the choice of the representation scheme that is used to encode a neural architecture in the form of a gene string (genotype) and to decode a genotype into the corresponding neural architecture (phenotype). The representation scheme used not only constrains the class of neural architectures that are representable (evolvable) in the system, but also determines the efficiency and the time-space complexity of the evolutionary design procedure as a whole. This paper identifies and discusses a set of properties that can be used to characterize different representations used in EDNA and to design or select representations with the necessary properties for particular classes of applications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129778. 	[Title] Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group [Abstract] The Building-Block Hypothesis appeals to the notion of problem decomposition and the assembly of solutions from sub-solutions. Accordingly, there have been many varieties of GA test problems with a structure based on building-blocks. Many of these problems use deceptive fitness functions to model interdependency between the bits within a block. However, very few have any model of interdependency between building-blocks; those that do are not consistent in the type of interaction used intra-block and inter-block. This paper discusses the inadequacies of the various test problems in the literature and clarifies the concept of building-block interdependency. We formulate a principled model of hierarchical interdependency that can be applied through many levels in a consistent manner and introduce Hierarchical If-and-only-if (H-IFF) as a canonical example. We present some empirical results of GAs on H-IFF showing that if population diversity is maintained and linkage is tight then the GA is able to identify and manipulate building-blocks over many levels of assembly, as the Building-Block Hypothesis suggests. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15670. 	[Title] Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal [Abstract] University of Wisconsin-Madison, Department of Computer Sciences Technical Report CS-TR-94-1259 Abstract The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) [46] is an extension of the Iterated Prisoner's Dilemma with evolution that allows players to choose and to refuse their game partners. From individual behaviors, behavioral population structures emerge. In this report, we examine one particular IPD/CR environment and document the social network methods used to identify population behaviors found within this complex adaptive system. In contrast to the standard homogeneous population of nice cooperators, we have also found metastable populations of mixed strategies within this environment. In particular, the social networks of interesting populations and their evolution are examined. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1137466. 	[Title] GREQE a Diplome des Etudes Approfondies en Economie Mathematique et Econometrie A Genetic Algorithm for [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153853. 	[Title] Voting for Schemata [Abstract] The schema theorem states that implicit parallel search is behind the power of the genetic algorithm. We contend that chromosomes can vote, proportionate to their fitness, for candidate schemata. We maintain a population of binary strings and ternary schemata. The string population not only works on solving its problem domain, but it supplies fitness for the schema population, which indirectly can solve the original problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116552. 	[Title] Empirical studies of the genetic algorithm with non-coding segments [Abstract] The genetic algorithm (GA) is a problem solving method that is modelled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of non-coding segments on GA performance. Non-coding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on non-coding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article, we discuss our hypotheses on non-coding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of non-coding segments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #263498. 	[Title] Automatic Design of Cellular Neural Networks by means of Genetic Algorithms: Finding a Feature Detector [Abstract] This paper aims to examine the use of genetic algorithms to optimize subsystems of cellular neural network architectures. The application at hand is character recognition: the aim is to evolve an optimal feature detector in order to aid a conventional classifier network to generalize across different fonts. To this end, a performance function and a genetic encoding for a feature detector are presented. An experiment is described where an optimal feature detector is indeed found by the genetic algorithm. We are interested in the application of cellular neural networks in computer vision. Genetic algorithms (GA's) [1-3] can serve to optimize the design of cellular neural networks. Although the design of the global architecture of the system could still be done by human insight, we propose that specific sub-modules of the system are best optimized using one or other optimization method. GAs are a good candidate to fulfill this optimization role, as they are well suited to problems where the objective function is a complex function of many parameters. The specific problem we want to investigate is one of character recognition. More specifically, we would like to use the GA to find optimal feature detectors to be used in the recognition of digits . Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112911. 	[Title] Evolution of Iteration in Genetic Programming D a v d A The solution to many [Abstract] This paper introduces the new operation of restricted iteration creation that automatically Genetic programming extends Holland's genetic algorithm to the task of automatic programming. Early work on genetic programming demonstrated that it is possible to evolve a sequence of work-performing steps in a single result-producing branch (that is, a one-part "main" program). The book Genetic Programming: On the Programming of Computers by Means of Natural Selection (Koza 1992) describes an extension of Holland's genetic algorithm in which the genetic population consists of computer programs (that is, compositions of primitive functions and terminals). See also Koza and Rice (1992). In the most basic form of genetic programming (where only a single result-producing branch is evolved), genetic programming demonstrated the capability to discover a sequence (as to both its length and its content) of work-performing steps that is sufficient to produce a satisfactory solution to several problems, including many problems that have been used over the years as benchmarks in machine learning and artificial intelligence. Before applying genetic programming to a problem, the user must perform five major preparatory steps, namely identifying the terminals (inputs) of the to-be-evolved programs, identifying the primitive functions (operations) contained in the to-be-evolved programs, creating the fitness measure for evaluating how well a given program does at solving the problem at hand, choosing certain control parameters (notably population size and number of generations to be run), and determining the termination criterion and method of result designation (typically the best-so-far individual from the populations produced during the run). creates a restricted iteration-performing Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #231249. 	[Title] Dynamic Hill Climbing: Overcoming the limita- tions of optimization techniques [Abstract] This paper describes a novel search algorithm, called dynamic hill climbing, that borrows ideas from genetic algorithms and hill climbing techniques. Unlike both genetic and hill climbing algorithms, dynamic hill climbing has the ability to dynamically change its coordinate frame during the course of an optimization. Furthermore, the algorithm moves from a coarse-grained search to a fine-grained search of the function space by changing its mutation rate and uses a diversity-based distance metric to ensure that it searches new regions of the space. Dynamic hill climbing is empirically compared to a traditional genetic algorithm using De Jong's well-known five function test suite [4] and is shown to vastly surpass the performance of the genetic algorithm, often finding better solutions using only 1% as many function evaluations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113438. 	[Title] Modeling the Evolution of Motivation [Abstract] In order for learning to improve the adaptiveness of an animal's behavior and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions influence its reproductive fitness. For example, many circumstances that damage an animal, or otherwise reduce its fitness are painful and tend to be avoided. We refer to the mechanism by which an animal evaluates the fitness consequences of its actions as a "motivation system," and argue that such a system must evolve along with the behaviors it evaluates. We describe simulations of the evolution of populations of agents instantiating a number of different architectures for generating action and learning, in worlds of differing complexity. We find that in some cases, members of the populations evolve motivation systems that are accurate enough to direct learning so as to increase the fitness of the actions the agents perform. Furthermore, the motivation systems tend to incorporate systematic distortions in their representations of the worlds they inhabit; these distortions can increase the adaptiveness of the behavior generated. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130847. 	[Title] A Genetic Algorithm for File and Task Placement in a Distributed System [Abstract] In this paper we explore the distributed file and task placement problem, which is intractable. We also discuss genetic algorithms and how they have been used successfully to solve combinatorial problems. Our experimental results show the GA to be far superior to the greedy heuristic in obtaining optimal and near optimal file and task placements for the problem with various data sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131360. 	[Title] Solving Combinatorial Problems Using Evolutionary Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128291. 	[Title] A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation [Abstract] We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search process. We investigate a collective memory search which utilizes a random search engine and find that it significantly outperforms the GP based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform better than a collective memory search employing random search as an engine. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130856. 	[Title] Chaos, Fractals, and Genetic Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128314. 	[Title] Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem [Abstract] We utilize collective memory to integrate weak and strong search heuristics to find cliques in FC, a family of graphs. We construct FC such that pruning of partial solutions will be ineffective. Each weak heuristic maintains a local cache of the collective memory. We examine the impact on the distributed search from the various characteristics of the distribution of the collective memory, the search algorithms, and our family of graphs. We find the distributed search performs better than the individuals, even though the space of partial solutions is combinatorially explosive. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128319. 	[Title] Modeling Distributed Search via Social Insects [Abstract] Complex group behavior arises in social insects colonies as the integration of the actions of simple and redundant individual insects [Adler and Gordon, 1992, Oster and Wilson, 1978]. Furthermore, the colony can act as an information center to expedite foraging [Brown, 1989]. We apply these lessons from natural systems to model collective action and memory in a computational agent society. Collective action can expedite search in combinatorial optimization problems [Dorigo et al., 1996]. Collective memory can improve learning in multi-agent systems [Garland and Alterman, 1996]. Our collective adaptation integrates the simplicity of collective action with the pattern detection of collective memory to significantly improve both the gathering and processing of knowledge. As a test of the role of the society as an information center, we examine the ability of the society to distribute task allocation without any omnipotent centralized control. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153943. 	[Title] Recombination Operator, its Correlation to the Fitness Landscape and Search Performance [Abstract] The author reserves all other publication and other rights in association with the copyright in the thesis, and except as hereinbefore provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author's prior written permission. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154459. 	[Title]  [Abstract] A Genetic Algorithm Tutorial Darrell Whitley Technical Report CS-93-103 (Revised) November 10, 1993 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1956. 	[Title] INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning [Abstract] The development of multistrategy learning systems should be based on a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter introduces the Inferential Theory of Learning that provides a conceptual framework for explaining logical capabilities of learning strategies, i.e., their competence. Viewing learning as a process of modifying the learners knowledge by exploring the learners experience, the theory postulates that any such process can be described as a search in a knowledge space, triggered by the learners experience and guided by learning goals. The search operators are instantiations of knowledge transmutations, which are generic patterns of knowledge change. Transmutations may employ any basic type of inferencededuction, induction or analogy. Several fundamental knowledge transmutations are described in a novel and general way, such as generalization, abstraction, explanation and similization, and their counterparts, specialization, concretion, prediction and dissimilization, respectively. Generalization enlarges the reference set of a description (the set of entities that are being described). Abstraction reduces the amount of the detail about the reference set. Explanation generates premises that explain (or imply) the given properties of the reference set. Similization transfers knowledge from one reference set to a similar reference set. Using concepts of the theory, a multistrategy task-adaptive learning (MTL) methodology is outlined, and illustrated b y an example. MTL dynamically adapts strategies to the learning task, defined by the input information, learners background knowledge, and the learning goal. It aims at synergistically integrating a whole range of inferential learning strategies, such as empirical generalization, constructive induction, deductive generalization, explanation, prediction, abstraction, and similization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152421. 	[Title]  [Abstract] A Genetic Algorithm Tutorial Darrell Whitley Technical Report CS-93-103 (Revised) November 10, 1993 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123756. 	[Title] Case-Based Probability Factoring in Bayesian Belief Networks [Abstract] Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #210871. 	[Title] Coevolving High-Level Representations [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #210872. 	[Title] An Evolutionary Algorithm that Constructs Recurrent Neural Networks [Abstract] Standard methods for inducing both the structure and weight values of recurrent neural networks fit an assumed class of architectures to every task. This simplification is necessary because the interactions between network structure and function are not well understood. Evolutionary computation, which includes genetic algorithms and evolutionary programming, is a population-based search method that has shown promise in such complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. This algorithms empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #197054. 	[Title] Evolving Obstacle Avoidance Behavior in a Robot Arm [Abstract] Existing approaches for learning to control a robot arm rely on supervised methods where correct behavior is explicitly given. It is difficult to learn to avoid obstacles using such methods, however, because examples of obstacle avoidance behavior are hard to generate. This paper presents an alternative approach that evolves neural network controllers through genetic algorithms. No input/output examples are necessary, since neuro-evolution learns from a single performance measurement over the entire task of grasping an object. The approach is tested in a simulation of the OSCAR-6 robot arm which receives both visual and sensory input. Neural networks evolved to effectively avoid obstacles at various locations to reach random target locations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #254923. 	[Title] A genetic prototype learner [Abstract] Supervised classification problems have received considerable attention from the machine learning community. We propose a novel genetic algorithm based prototype learning system, PLEASE, for this class of problems. Given a set of prototypes for each of the possible classes, the class of an input instance is determined by the prototype nearest to this instance. We assume ordinal attributes and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space as determined by corresponding feature-value pairs. Comparisons with C4.5 on a set of artificial problems of controlled complexity demonstrate the effectiveness of the pro posed system. Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #1152508. 	[Title]  [Abstract] A lower-bound result on the power of Abstract This paper presents a lower-bound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove that for the class of monotonic functions, the algorithm finds the optimal solution, and does so with an exponential convergence rate. The analysis pertains to the ideal behavior of the algorithm where the main task reduces to showing convergence of probability distributions over the search space of combinatorial structures to the optimal one. We take exponential convergence to be indicative of efficient solvability for the sample-bounded algorithm, although a sampling theory is needed to better relate the limit behavior to actual behavior. The paper concludes with a discussion of some immediate problems that lie ahead. a genetic algorithm Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #1128959. 	[Title] On the Relations Between Search and Evolutionary Algorithms [Abstract] Technical Report: CSRP-96-7 March 1996 Abstract Evolutionary algorithms are powerful techniques for optimisation whose operation principles are inspired by natural selection and genetics. In this paper we discuss the relation between evolutionary techniques, numerical and classical search methods and we show that all these methods are instances of a single more general search strategy, which we call the `evolutionary computation cookbook'. By combining the features of classical and evolutionary methods in different ways new instances of this general strategy can be generated, i.e. new evolutionary (or classical) algorithms can be designed. One such algorithm, GA fl , is described. Please summarise all the given information.

Paper #54129 has the title `Strongly Typed Genetic Programming  
` and the abstract `BBN Technical Report #7866: Abstract Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of the same data type. To correct this deficiency, we introduce a variation of genetic programming called "strongly typed" genetic programming (STGP). In STGP, variables, constants, arguments, and returned values can be of any data type with the provision that the data type for each such value be specified beforehand. This allows the initialization process and the genetic operators to only generate syntactically correct parse trees. Key concepts for STGP are generic functions, which are not true strongly typed functions but rather templates for classes of such functions, and generic data types, which are analogous. To illustrate STGP, we present four examples involving vector/matrix manipulation and list manipulation: (1) the multi-dimensional least-squares regression problem, (2) the multi-dimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.
`.
Question: Which category should Paper #54129 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #54129 has the title `Strongly Typed Genetic Programming  
` and the abstract `BBN Technical Report #7866: Abstract Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of the same data type. To correct this deficiency, we introduce a variation of genetic programming called "strongly typed" genetic programming (STGP). In STGP, variables, constants, arguments, and returned values can be of any data type with the provision that the data type for each such value be specified beforehand. This allows the initialization process and the genetic operators to only generate syntactically correct parse trees. Key concepts for STGP are generic functions, which are not true strongly typed functions but rather templates for classes of such functions, and generic data types, which are analogous. To illustrate STGP, we present four examples involving vector/matrix manipulation and list manipulation: (1) the multi-dimensional least-squares regression problem, (2) the multi-dimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.
`.
Question: Which category should Paper #54129 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms