Above are papers related to the following paper: [Paper_ID] Paper #81714. 	[Title] EVOLVING REPRESENTATIONS OF DESIGN CASES AND THEIR USE IN CREATIVE DESIGN [Abstract] In case-based design, the adaptation of a design case to new design requirements plays an important role. If it is sufficient to adapt a predefined set of design parameters, the task is easily automated. If, however, more far-reaching, creative changes are required, current systems provide only limited success. This paper describes an approach to creative design adaptation based on the notion of creativity as 'goal oriented shift of focus of a search process'. An evolving representation is used to restructure the search space so that designs similar to the example case lie in the focus of the search. This focus is than used as a starting point to create new designs. 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 #1128198. 	[Title] Strategy Adaptation by Competing Subpopulations [Abstract] The breeder genetic algorithm BGA depends on a set of control parameters and genetic operators. In this paper it is shown that strategy adaptation by competing subpopulations makes the BGA more robust and more efficient. Each subpopulation uses a different strategy which competes with other subpopulations. Numerical results are pre sented for a number of test functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #98698. 	[Title] Adaptive Strategy Selection for Concept Learning [Abstract] In this paper, we explore the use of genetic algorithms (GAs) to construct a system called GABIL that continually learns and refines concept classification rules from its interac - tion with the environment. The performance of this system is compared with that of two other concept learners (NEWGEM and C4.5) on a suite of target concepts. From this comparison, we identify strategies responsible for the success of these concept learners. We then implement a subset of these strategies within GABIL to produce a multistrategy concept learner. Finally, this multistrategy concept learner is further enhanced by allowing the GAs to adaptively select the appropriate strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112075. 	[Title] In Proceedings of the 1997 Sian Kaan International Workshop on Neural Networks and Neurocontrol. Real-Valued [Abstract] 2 Neural Network & Machine Learning Laboratory Computer Science Department Brigham Young University Provo, UT 84602, USA Email: martinez@cs.byu.edu WWW: http://axon.cs.byu.edu Abstract. Many neural network models must be trained by finding a set of real-valued weights that yield high accuracy on a training set. Other learning models require weights on input attributes that yield high leave-one-out classification accuracy in order to avoid problems associated with irrelevant attributes and high dimensionality. In addition, there are a variety of general problems for which a set of real values must be found which maximize some evaluation function. This paper presents an algorithm for doing a schemata search over a real-valued weight space to find a set of weights (or other real values) that yield high values for a given evaluation function. The algorithm, called the Real-Valued Schemata Search (RVSS), uses the BRACE statistical technique [Moore & Lee, 1993] to determine when to narrow the search space. This paper details the RVSS approach and gives initial empirical results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129367. 	[Title] Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work [Abstract] We review recent work done by our group on applying genetic algorithms (GAs) to the design of cellular automata (CAs) that can perform computations requiring global coordination. A GA was used to evolve CAs for two computational tasks: density classification and synchronization. In both cases, the GA discovered rules that gave rise to sophisticated emergent computational strategies. These strategies can be analyzed using a "computational mechanics" framework in which "particles" carry information and interactions between particles effects information processing. This framework can also be used to explain the process by which the strategies were designed by the GA. The work described here is a first step in employing GAs to engineer useful emergent computation in decentralized multi-processor systems. It is also a first step in understanding how an evolutionary process can produce complex systems with sophisticated collective computational abilities. 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 #568857. 	[Title] Evolving Self-Supporting Structures Page 18 References Evolution of Visual Control Systems for Robots. To appear [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #573978. 	[Title] Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation [Abstract] Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the major difference to Genetic Algorithms, since it allows for an on-line adaptation of strategy parameters without exogenous control. 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 #1129629. 	[Title] of a simulator for evolving morphology are: Universal the simulator should cover an infinite gen [Abstract] Funes, P. and Pollack, J. (1997) Computer Evolution of Buildable Objects. Fourth European Conference on Artificial Life. P. Husbands and I. Harvey, eds., MIT Press. pp 358-367. knowledge into the program, which would result in familiar structures, we provided the algorithm with a model of the physical reality and a purely utilitarian fitness function, thus supplying measures of feasibility and functionality. In this way the evolutionary process runs in an environment that has not been unnecessarily constrained. We added, however, a requirement of computability to reject overly complex structures when they took too long for our simulations to evaluate. The results are encouraging. The evolved structures had a surprisingly alien look: they are not based in common knowledge on how to build with brick toys; instead, the computer found ways of its own through the evolutionary search process. We were able to assemble the final designs manually and confirm that they accomplish the objectives introduced with our fitness functions. After some background on related problems, we describe our physical simulation model for two-dimensional Lego structures, and the representation for encoding them and applying evolution. We demonstrate the feasibility of our work with photos of actual objects which were the result of particular optimizations. Finally, we discuss future work and draw some conclusions. In order to evolve both the morphology and behavior of autonomous mechanical devices which can be manufactured, one must have a simulator which operates under several constraints, and a resultant controller which is adaptive enough to cover the gap between simulated and real world. eral space of mechanisms. Conservative - because simulation is never perfect, it should preserve a margin of safety. Efficient - it should be quicker to test in simulation than through physical production and test. Buildable - results should be convertible from a simula tion to a real object Computer Evolution of Buildable Objects Abstract The idea of co-evolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has been constrained by the reality gap which implies that resultant objects are usually not buildable. The work we present takes a step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of parts. Evolution takes place in a simulator we designed, which computes forces and stresses and predicts failure for 2-dimensional Lego structures. The final printout of our program is a schematic assembly, which can then be built physically. We demonstrate its functionality in several different evolved entities. 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 #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 #513189. 	[Title] Extended Selection Mechanisms in Genetic Algorithms [Abstract]  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 #576691. 	[Title] Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first [Abstract] In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the search process. First experimental results are presented, which indicate that environment-dependent self-adaptation of appropriate settings for the mutation rate is possible even for GAs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #84021. 	[Title] Using Genetic Algorithms for Supervised Concept Learning [Abstract] Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this chapter we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is implemented that learns a concept from a set of positive and negative examples. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results support that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity. 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 #70970. 	[Title] Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces [Abstract] Let us call a non-deterministic incremental algorithm one that is able to construct any solution to a combinatorial problem by selecting incrementally an ordered sequence of choices that defines this solution, each choice being made non-deterministically. In that case, the state space can be represented as a tree, and a solution is a path from the root of that tree to a leaf. This paper describes how the simulated evolution of a population of such non-deterministic incremental algorithms offers a new approach for the exploration of a state space, compared to other techniques like Genetic Algorithms (GA), Evolutionary Strategies (ES) or Hill Climbing. In particular, the efficiency of this method, implemented as the Evolving Non-Determinism (END) model, is presented for the sorting network problem, a reference problem that has challenged computer science. Then, we shall show that the END model remedies some drawbacks of these optimization techniques and even outperforms them for this problem. Indeed, some 16-input sorting networks as good as the best known have been built from scratch, and even a 25-year-old result for the 13-input problem has been improved by one comparator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #81722. 	[Title] EMERGENT BEHAVIOUR IN CO-EVOLUTIONARY DESIGN [Abstract] An important aspect of creative design is the concept of emergence. Though emergence is important, its mechanism is either not well understood or it is limited to the domain of shapes. This deficiency can be compensated by considering definitions of emergent behaviour from the Artificial Life (ALife) research community. With these new insights, it is proposed that a computational technique, called evolving representations of design genes, can be extended to emergent behaviour. We demonstrate emergent be-haviour in a co-evolutionary model of design. This co-evolutionary approach to design allows a solution space (structure space) to evolve in response to a problem space (be-haviour space). Since the behaviour space is now an active participant, behaviour may emerge with new structures at the end of the design process. This paper hypothesizes that emergent behaviour can be identified using the same technique. The floor plan example of (Gero & Schnier 1995) is extended to demonstrate how behaviour can emerge in a co-evolutionary design process. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38205. 	[Title] Competitive Environments Evolve Better Solutions for Complex Tasks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #59715. 	[Title] Genetic Programming Exploratory Power and the Discovery of Functions [Abstract] Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution process from the dual perspective of diversity and causality. From a static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. From a dynamic point of view, an analysis of the causality of the crossover operator suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #608326. 	[Title] A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction [Abstract] In the field of Operation Research and Artificial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search (Zhigljavsky 1991). Basically, those techniques iteratively sample the search space with respect to a probability distribution which is updated according to the result of previous samples and some predefined strategy. Genetic Algorithms (GAs) (Goldberg 1989) or Greedy Randomized Adaptive Search Procedures (GRASP) (Feo & Resende 1995) are two particular instances of this paradigm. In this paper, we present SAGE, a search algorithm based on the same fundamental mechanisms as those techniques. However, it addresses a class of problems for which it is difficult to design transformation operators to perform local search because of intrinsic constraints in the definition of the problem itself. For those problems, a procedural approach is the natural way to construct solutions, resulting in a state space represented as a tree or a DAG. The aim of this paper is to describe the underlying heuristics used by SAGE to address problems belonging to that class. The performance of SAGE is analyzed on the problem of grammar induction and its successful application to problems from the recent Abbadingo DFA learning competition is presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #307015. 	[Title] Optimal Mutation Rates in Genetic Search [Abstract] The optimization of a single bit string by means of iterated mutation and selection of the best (a (1+1)-Genetic Algorithm) is discussed with respect to three simple fitness functions: The counting ones problem, a standard binary encoded integer, and a Gray coded integer optimization problem. A mutation rate schedule that is optimal with respect to the success probability of mutation is presented for each of the objective functions, and it turns out that the standard binary code can hamper the search process even in case of unimodal objective functions. While normally a mutation rate of 1=l (where l denotes the bit string length) is recommendable, our results indicate that a variation of the mutation rate is useful in cases where the fitness function is a multimodal pseudo-boolean function, where multimodality may be caused by the objective function as well as the encoding mechanism. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134219. 	[Title] An Overview of Evolutionary Computation [Abstract] Evolutionary computation uses computational models of evolution - ary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and di fferences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research. 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 #503893. 	[Title] An Evolutionary Approach to Combinatorial Optimization Problems [Abstract] The paper reports on the application of genetic algorithms, probabilistic search algorithms based on the model of organic evolution, to NP-complete combinatorial optimization problems. In particular, the subset sum, maximum cut, and minimum tardy task problems are considered. Except for the fitness function, no problem-specific changes of the genetic algorithm are required in order to achieve results of high quality even for the problem instances of size 100 used in the paper. For constrained problems, such as the subset sum and the minimum tardy task, the constraints are taken into account by incorporating a graded penalty term into the fitness function. Even for large instances of these highly multimodal optimization problems, an iterated application of the genetic algorithm is observed to find the global optimum within a number of runs. As the genetic algorithm samples only a tiny fraction of the search space, these results are quite encouraging. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #595157. 	[Title] Investigating the Generality of Automatically Defined Functions [Abstract] This paper studies how well the combination of simulated annealing and ADFs solves genetic programming (GP) style program discovery problems. On a suite composed of the even-k-parity problems for k = 3,4,5, it analyses the performance of simulated annealing with ADFs as compared to not using ADFs. In contrast to GP results on this suite, when simulated annealing is run with ADFs, as problem size increases, the advantage to using them over a standard GP program representation is marginal. When the performance of simulated annealing is compared to GP with both algorithm using ADFs on the even-3-parity problem GP is advantageous, on the even-4-parity problem SA and GP are equal, and on the even-5-parity problem SA is advantageous. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128151. 	[Title] Learning Representations for Evolutionary Computation an example from the domain of two-dimensional shape designs. In [Abstract] Evolutionary systems have been used in a variety of applications, from turbine design to scheduling problems. The basic algorithms are similar in all these applications, but the representation is always problem specific. Unfortunately, the search time for evolutionary systems very much depends on efficient codings, using problem specific domain knowledge to reduce the size of the search space. This paper describes an approach, where the user only specifies a very general, basic coding that can be used in a larger variety of problems. The system then learns a more efficient, problem specific coding. To do this, an evolutionary system with variable length coding is used. While the system optimizes an example problem, a meta process identifies successful combinations of genes in the population and combines them into higher level evolved genes. The extraction is repeated iteratively, allowing genes to evolve that have a high level complexity and encode a high number of the original, basic genes. This results in a continuous restructuring of the search space, allowing potentially successful solutions to be found in much shorter search time. The evolved coding can then be used to solve other, related problems. While not excluding any potentially desirable solutions, the evolved coding makes knowledge from the example problem available for the new problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119708. 	[Title] On The State of Evolutionary Computation [Abstract] In the past few years the evolutionary computation landscape has been rapidly changing as a result of increased levels of interaction between various research groups and the injection of new ideas which challenge old tenets. The effect has been simultaneously exciting, invigorating, annoying, and bewildering to the old-timers as well as the new-comers to the field. Emerging out of all of this activity are the beginnings of some structure, some common themes, and some agreement on important open issues. We attempt to summarize these emergent properties in this paper. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #44514. 	[Title] The Observers Paradox: Apparent Computational Complexity in Physical Systems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #575077. 	[Title] Fitness Landscapes and Difficulty in Genetic Programming [Abstract] The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an attempt to determine which landscape measures correlate with the difficulty of the problem. The autocorrelation of the fitness values of random walks, a measure which has been shown to be related to perceived difficulty using other techniques, is only a weak indicator of the difficulty as perceived by genetic programming. All of these problems show unusually low autocorrelation. Comparison of the range of landscape basin depths at the end of adaptive walks on the landscapes shows good correlation with problem difficulty, over the entire range of problems examined. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152358. 	[Title] Crossover or Mutation? [Abstract] Genetic algorithms rely on two genetic operators crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82920. 	[Title] A Survey of Evolution Strategies [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127913. 	[Title] THE OPTIONS DESIGN EXPLORATION SYSTEM Reference Manual and User Guide Version B2.1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125492. 	[Title] Numerical techniques for efficient sonar bearing and range searching in the near field using genetic algorithms [Abstract] This article describes a numerical method that may be used to efficiently locate and track underwater sonar targets in the near-field, with both bearing and range estimation, for the case of very large passive arrays. The approach used has no requirement for a priori knowledge about the source and uses only limited information about the receiver array shape. The role of sensor position uncertainty and the consequence of targets always being in the near-field are analysed and the problems associated with the manipulation of large matrices inherent in conventional eigenvalue type algorithms noted. A simpler numerical approach is then presented which reduces the problem to that of search optimization. When using this method the location of a target corresponds to finding the position of the maximum weighted sum of the output from all sensors. Since this search procedure can be dealt with using modern stochastic optimization methods, such as the genetic algorithm, the operational requirement that an acceptable accuracy be achieved in real time can usually be met. The array studied here consists of 225 elements positioned along a flexible cable towed behind a ship with 3.4m between sensors, giving an effective aperture of 761.6m. For such a long array, the far field assumption used in most beam-forming algorithms is no longer appropriate. The waves emitted by the targets then have to be considered as curved rather than plane. It is shown that, for simulated data, if no significant noise Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35061. 	[Title] Evolving Networks: Using the Genetic Algorithm with Connectionist Learning [Abstract]  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 #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #447224. 	[Title] A COMPRESSION ALGORITHM FOR PROBABILITY TRANSITION MATRICES [Abstract] This paper describes a compression algorithm for probability transition matrices. The compressed matrix is itself a probability transition matrix. In general the compression is not error-free, but the error appears to be small even for high levels of compression. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198653. 	[Title] Information filtering: Selection mechanisms in learning systems. Machine Learning, 10:113-151, 1993. Generalization as search. Artificial [Abstract]  Please summarise all the given information.

Paper #81714 has the title `EVOLVING REPRESENTATIONS OF DESIGN CASES AND THEIR USE IN CREATIVE DESIGN  
` and the abstract `In case-based design, the adaptation of a design case to new design requirements plays an important role. If it is sufficient to adapt a predefined set of design parameters, the task is easily automated. If, however, more far-reaching, creative changes are required, current systems provide only limited success. This paper describes an approach to creative design adaptation based on the notion of creativity as 'goal oriented shift of focus of a search process'. An evolving representation is used to restructure the search space so that designs similar to the example case lie in the focus of the search. This focus is than used as a starting point to create new designs. 
`.
Question: Which category should Paper #81714 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #81714 has the title `EVOLVING REPRESENTATIONS OF DESIGN CASES AND THEIR USE IN CREATIVE DESIGN  
` and the abstract `In case-based design, the adaptation of a design case to new design requirements plays an important role. If it is sufficient to adapt a predefined set of design parameters, the task is easily automated. If, however, more far-reaching, creative changes are required, current systems provide only limited success. This paper describes an approach to creative design adaptation based on the notion of creativity as 'goal oriented shift of focus of a search process'. An evolving representation is used to restructure the search space so that designs similar to the example case lie in the focus of the search. This focus is than used as a starting point to create new designs. 
`.
Question: Which category should Paper #81714 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: Case_Based