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 #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 #273152. 	[Title] Evolutionary Module Acquisition [Abstract]  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 #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 #573964. 	[Title] Optimization by Means of Genetic Algorithms [Abstract] Genetic Algorithms (GAs) are powerful heuristic search strategies based upon a simple model of organic evolution. The basic working scheme of GAs as developed by Holland [Hol75] is described within this paper in a formal way, and extensions based upon the second-level learning principle for strategy parameters as introduced in Evolution Strategies (ESs) are proposed. First experimental results concerning this extension of GAs are also reported. 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 #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 #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 #152483. 	[Title] An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms [Abstract] The increasing availability of finely-grained parallel architectures has resulted in a variety of evolutionary algorithms (EAs) in which the population is spatially distributed and local selection algorithms operate in parallel on small, overlapping neighborhoods. The effects of design choices regarding the particular type of local selection algorithm as well as the size and shape of the neighborhood are not particularly well understood and are generally tested empirically. In this paper we extend the techniques used to more formally analyze selection methods for sequential EAs and apply them to local neighborhood models, resulting in a much clearer understanding of the effects of neighborhood size and shape. 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 #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 #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 #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 #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 #577086. 	[Title] A Search Space Analysis of the Job Shop Scheduling Problem [Abstract] A computational study for the Job Shop Scheduling Problem is presented. Thereby emphasis is put on the structure of the solution space as it appears for adaptive search. A statistical analysis of the search spaces reveals the impacts of inherent properties of the problem on adaptive heuristics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #682815. 	[Title] The Role of Development in Genetic Algorithms [Abstract] Technical Report Number CS94-394 Computer Science and Engineering, U.C.S.D. Abstract The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representational spaces are identical. We argue that a careful analysis of developmental mechanisms is useful when understanding the success of several standard GA techniques, and can clarify the relationships between more recently proposed enhancements. We provide a framework which distinguishes between two developmental mechanisms | learning and maturation | while also showing several common effects on GA search. This framework is used to analyze how maturation and local search can change the dynamics of the GA. We observe that in some contexts, maturation and local search can be incorporated into the fitness evaluation, but illustrate reasons for considering them seperately. Further, we identify contexts in which maturation and local search can be distinguished from the fitness evaluation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593091. 	[Title] A Survey of Parallel Genetic Algorithms [Abstract] IlliGAL Report No. 97003 May 1997 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 #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 #787016. 	[Title] Genetic Self-Learning [Abstract] Evolutionary Algorithms are direct random search algorithms which imitate the principles of natural evolution as a method to solve adaptation (learning) tasks in general. As such they have several features in common which can be observed on the genetic and phenotypic level of living species. In this paper the algorithms' capability of adaptation or learning in a wider sense is demonstrated, and it is focused on Genetic Algorithms to illustrate the learning process on the population level (first level learning), and on Evolution Strategies to demonstrate the learning process on the meta-level of strategy parameters (second level learning). 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 #561238. 	[Title] Putting the Genetics back into Genetic Algorithms [Abstract]  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 #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 #1125597. 	[Title] Pruning backpropagation neural networks using modern stochastic optimization techniques [Abstract] Approaches combining genetic algorithms and neural networks have received a great deal of attention in recent years. As a result, much work has been reported in two major areas of neural network design: training and topology optimization. This paper focuses on the key issues associated with the problem of pruning a multilayer perceptron using genetic algorithms and simulated annealing. The study presented considers a number of aspects associated with network training that may alter the behavior of a stochastic topology optimizer. Enhancements are discussed that can improve topology searches. Simulation results for the two mentioned stochastic optimization methods applied to nonlinear system identification are presented and compared with a simple random search. 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 #640617. 	[Title] Staged Hybrid Genetic Search for Seismic Data Imaging [Abstract] Seismic data interpretation problems are typically solved using computationally intensive local search methods which often result in inferior solutions. Here, a traditional hybrid genetic algorithm is compared with different staged hybrid genetic algorithms on the geophysical imaging static corrections problem. The traditional hybrid genetic algorithm used here applied local search to every offspring produced by genetic search. The staged hybrid genetic algorithms were designed to temporally separate the local and genetic search components into distinct phases so as to minimize interference between the two search methods. The results show that some staged hybrid genetic algorithms produce higher quality solutions while using significantly less computational time for this problem. 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 #85352. 	[Title] Evolution in Time and Space The Parallel Genetic Algorithm [Abstract] The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live in a 2-D world. Selection of a mate is done by each individual independently in its neighborhood. Secondly, each individual may improve its fitness during its lifetime by e.g. local hill-climbing. The PGA is totally asynchronous, running with maximal efficiency on MIMD parallel computers. The search strategy of the PGA is based on a small number of active and intelligent individuals, whereas a GA uses a large population of passive individuals. We will investigate the PGA with deceptive problems and the traveling salesman problem. We outline why and when the PGA is succesful. Abstractly, a PGA is a parallel search with information exchange between the individuals. If we represent the optimization problem as a fitness landscape in a certain configuration space, we see, that a PGA tries to jump from two local minima to a third, still better local minima, by using the crossover operator. This jump is (probabilistically) successful, if the fitness landscape has a certain correlation. We show the correlation for the traveling salesman problem by a configuration space analysis. The PGA explores implicitly the above correlation. 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 #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 #66556. 	[Title] The Schema Theorem and Price's Theorem [Abstract] Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata re-emerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a "missing" schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of "adaptive landscape" analysis is examined and counterexamples offered to the commonly used correlation statistic. Instead, an alternative statistic | the transmission function in the fitness domain | is proposed as the optimal statistic for estimating GA performance from limited samples. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #574462. 	[Title] Genetic Algorithm Programming Environments [Abstract] Interest in Genetic algorithms is expanding rapidly. This paper reviews software environments for programming Genetic Algorithms ( GA s). As background, we initially preview genetic algorithms' models and their programming. Next we classify GA software environments into three main categories: Application-oriented, Algorithm-oriented and ToolKits. For each category of GA programming environment we review their common features and present a case study of a leading environment. Please summarise all the given information.

Paper #576691 has the title `Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first
` and the 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. 
`.
Question: Which category should Paper #576691 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #576691 has the title `Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first
` and the 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. 
`.
Question: Which category should Paper #576691 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