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 #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 #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 #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 #6346. 	[Title] Instance Pruning Techniques [Abstract] The nearest neighbor algorithm and its derivatives are often quite successful at learning a concept from a training set and providing good generalization on subsequent input vectors. However, these techniques often retain the entire training set in memory, resulting in large memory requirements and slow execution speed, as well as a sensitivity to noise. This paper provides a discussion of issues related to reducing the number of instances retained in memory while maintaining (and sometimes improving) generalization accuracy, and mentions algorithms other researchers have used to address this problem. It presents three intuitive noise-tolerant algorithms that can be used to prune instances from the training set. In experiments on 29 applications, the algorithm that achieves the highest reduction in storage also results in the highest generalization accuracy of the three methods. 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 #33904. 	[Title] Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms [Abstract] We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA. fl Department of Computer Science, University of California at Berkeley. Supported by a NASA Graduate Fellowship. This paper was written while the author was a visiting researcher at the Ecole Normale Superieure-rue d'Ulm, Groupe de BioInformatique, France. E-mail: juels@cs.berkeley.edu y Department of Mathematics, University of California at Berkeley. Supported by an NDSEG Graduate Fellowship. E-mail: wattenbe@math.berkeley.edu 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 #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 #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 #646837. 	[Title] An Evolutionary Heuristic for the Minimum Vertex Cover Problem [Abstract]  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 #134199. 	[Title] Using Markov Chains to Analyze GAFOs [Abstract] Our theoretical understanding of the properties of genetic algorithms (GAs) being used for function optimization (GAFOs) is not as strong as we would like. Traditional schema analysis provides some first order insights, but doesn't capture the non-linear dynamics of the GA search process very well. Markov chain theory has been used primarily for steady state analysis of GAs. In this paper we explore the use of transient Markov chain analysis to model and understand the behavior of finite population GAFOs observed while in transition to steady states. This approach appears to provide new insights into the circumstances under which GAFOs will (will not) perform well. Some preliminary results are presented and an initial evaluation of the merits of this approach is provided. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #164885. 	[Title] Analyzing GAs Using Markov Models with Semantically Ordered and Lumped States [Abstract] At the previous FOGA workshop, we presented some initial results on using Markov models to analyze the transient behavior of genetic algorithms (GAs) being used as function optimizers (GAFOs). In that paper, the states of the Markov model were ordered via a simple and mathematically convenient lexicographic ordering used initially by Nix and Vose. In this paper, we explore alternative orderings of states based on interesting semantic properties such as average fitness, degree of homogeneity, average attractive force, etc. We also explore lumping techniques for reducing the size of the state space. Analysis of these reordered and lumped Markov models provides new insights into the transient behavior of GAs in general and GAFOs in particular. 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.

Paper #134219 has the title `An Overview of Evolutionary Computation  
` and the 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.
`.
Question: Which category should Paper #134219 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #134219 has the title `An Overview of Evolutionary Computation  
` and the 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.
`.
Question: Which category should Paper #134219 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