Foundations of genetic algorithms

著者

• Rawlins, Gregory J. E.
• Whitley, L. Darrell
• Vose, Michael D.
• Belew, Richard K.
• Banzhaf, Wolfgang
• Reeves, Colin R.
• Martin, W. N. (Worthy N.)
• William, M. Spears
• Jong, Kenneth A. de
• Poli, Riccardo
• Rowe, Jonathan E.
• Workshop on the Foundations of Genetic Algorithms and Classifier Systems
• Workshop on the Foundations of Genetic Algorithms

書誌事項

Foundations of genetic algorithms

edited by Gregory J.E. Rawlins

Morgan Kaufmann Publishers, c1991-

• [1]
• 2
• 3
• 4
• 5
• 6
• 7

注記

Includes bibliographies and indexes

"The first workshop on the foundations of genetic algorithms and classifier systems (FOGA/CS-90) was held July 15-18, 1990 on the Bloomington campus of Indiana University"--Introd

"The second workshop on Foundations of Genetic Algorithms (FOGA) was held July 26-29, 1992 in Vail, Colorado. Like the first FOGA workshop heldin 1990 (Rawlins, 1991), the purpose of this workshop is to create a forumfor theoretical work on genetic algorithms."--Introd

"The third workshop on Foundations of Genetic Algorithms (FOGA) was held July 31 through August 2, 1994, in Estes Park, Colorado."AL

"The fourth Foundations of Genetic Algorithms Workshop (FOGA) was held August 2 through August 5, 1996, in Alcala Park, San Diego"

1st pub. in 1991 , 341 p : FOGA-90 : 1990 : Indiana Univ. Bloomington and 2nd pub. in 1993 , 322 p : FOGA-92 : 1992 : Vail, Colorado

3rd pub. in 1995 , 366 p : FOGA-94 : 1994 : Estes Park, Colorado

4th pub. in 1997 , 463 p : FOGA-96 : 1996 : Alcala Park, San Diego

5th pub. in 1999 , 316 p : FOGA-98 : 1998 : Amsterdam, Netherland

6th pub. in 2001 , 342 p : FOGA-2000 : 2000 : Charlottesville, Virginia

7th pub. in 2003 , 405 p : FOGA-2002 : 2002 : Torremolinos, Spain

3. / edited by L. Darrell Whitley and Michael D. Vose

5. / edited by Wolfgang Banzhaf and Colin Reeves

6. / edited by Worthy N. Martin and William M. Spears

7. / edited by Kenneth A. de Jong, Riccardo Poli and Jonathan E. Rowe

6 / place is San Francisco, Calif

内容説明・目次

巻冊次

7 ISBN 9780122081552

内容説明

Foundations of Genetic Algorithms, Volume 7 (FOGA-7) is a collection of 22 papers written by the field's leading researchers, representing the most current, state-of-the-art research both in GAs and in evolutionary computation theory in general. Much more than proceedings, this clothbound book and its companion six volumes document the bi-annual FOGA workshops since their inception in 1990. Before publication, each paper is peer reviewed, revised, and edited. Covering the variety of analysis tools and techniques that characterize the behavior of evolutionary algorithms, the FOGA series, with its brand-new volume 7, provides the single best source of reference for the theoretical work in this field.

目次

• Editorial Introduction
• Schema Analysis of OneMax Problem: Evolution Equation for First Order Schemata
• Partitioning, Epistasis, and Uncertainty
• A Schema-theory-based Extension of Geiringer's Theorem for Linear GP and Variable-length GAs under Homologous Crossover
• Bistability in a Gene Pool GA with Mutation
• The 'Crossover Landscape' and the !YHamming Landscape!| for Binary Search Spaces
• Modelling Finite Populations
• The Sensitivity of PBIL to Its Learning Rate, and How Detailed Balance Can Remove It
• Evolutionary Algorithms and the Boltzmann Distribution
• Modeling and Simulating Diploid Simple Genetic Algorithms
• On the Evolution of Phenotypic Exploration Distributions
• How many Good Programs are there? How Long are they?
• Modeling Variation in Cooperative Coevolution Using Evolutionary Game Theory
• A Mathematical Framework for the Study of Coevolution
• Guaranteeing Coevolutionary Objective Measures
• A New Framework for the Valuation of Algorithms for Black-Box Optimization
• A Study on the Performance of the (1+1)-Evolutionary Algorithm
• The Long Term Behavior of Genetic Algorithms with Stochastic Evaluation
• On the Behavior of fvfYf(1)fzfnfUEfwf{ES Optimizing Functions Disturbed by Generalized Noise
• Parameter Perturbation Mechanisms in Binary Coded GAs with Self-Adaptive Mutation
• Fitness Gains and Mutation Patterns: Deriving Mutation Rates by Exploiting Landscape Data
• Towards Qualitative Models of Interactions in Evolutionary Algorithms
• Genetic Search Reinforced by the Population Hierarchy

巻冊次

[1] ISBN 9781558601703

内容説明

Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems. This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; conditions for implicit parallelism; and analysis of multi-point crossover are also elaborated. This text likewise covers the genetic algorithms for real parameter optimization and isomorphisms of genetic algorithms. This publication is a good reference for students and researchers interested in genetic algorithms.

目次

Part 1: Genetic Algorithm Hardness The Nonuniform Walsh-Schema Transform Epistasis Variance: A Viewpoint on GA-Hardness Deceptiveness and Genetic Algorithm Dynamics Part 2: Selection and Convergence An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms A Comparative Analysis of Selection Schemes Used in Genetic Algorithms A Study of Reproduction in Generational and Steady State Genetic Algorithms Spurious Correlations and Premature Convergence in Genetic Algorithms Part 3: Classifier Systems Representing Attribute-Based Concepts in a Classifier System Quasimorphisms or Queasymorphisms? Modeling Finite Automaton Environments Variable Default Hierarchy Separation in a Classifier System Part 4: Coding and Representation A Hierarchical Approach to Learning the Boolean Multiplexer Function A Grammar-Based Genetic Algorithm Genetic Algorithms for Real Parameter Optimization Part 5: Framework Issues Fundamental Principles of Deception in Genetic Search Isomorphisms of Genetic Algorithms Conditions for Implicit Parallelism Part 6: Variation and Recombination The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination Genetic Operators for Sequencing Problems An Analysis of Multi-Point Crossover Evolution in Time and Space-The Parallel Genetic Algorithm Author Index Key Word Index

巻冊次

2 ISBN 9781558602632

内容説明

Foundations of Genetic Algorithms, Volume 2 provides insight of theoretical work in genetic algorithms. This book provides a general understanding of a canonical genetic algorithm. Organized into six parts encompassing 19 chapters, this volume begins with an overview of genetic algorithms in the broader adaptive systems context. This text then reviews some results in mathematical genetics that use probability distributions to characterize the effects of recombination on multiple loci in the absence of selection. Other chapters examine the static building block hypothesis (SBBH), which is the underlying assumption used to define deception. This book discusses as well the effect of noise on the quality of convergence of genetic algorithms. The final chapter deals with the primary goal in machine learning and artificial intelligence, which is to dynamically and automatically decompose problems into simpler problems to facilitate their solution. This book is a valuable resource for theorists and genetic algorithm researchers.

目次

Introduction Part 1: Foundation Issues Revisited Genetic Algorithms are Not Function Optimizers Generation Gaps Revisited Part 2: Modeling Genetic Algorithms Recombination Distributions for Genetic Algorithms An Executable Model of a Simple Genetic Algorithm Modeling Simple Genetic Algorithms Part 3: Deception and the Building Block Hypothesis Deception Considered Harmful Analyzing Deception in Trap Functions Relative Building-Block Fitness and the Building Block Hypothesis Part 4: Convergence and Genetic Diversity Accounting for Noise in the Sizing of Populations Population Diversity in an Immune System Model: Implications for Genetic Search Remapping Hyperspace During Genetic Search: Canonical Delta Folding Part 5: Genetic Operators and Their Analysis Real-Coded Genetic Algorithms and Interval-Schemata Genetic Set Recombination Crossover or Mutation? Simulated Crossover in Genetic Algorithms Part 6: Machine Learning Learning Boolean Functions with Genetic Algorithms: A PAC Analysis Is the Genetic Algorithm a Cooperative Learner? Hierarchical Automatic Function Definition in Genetic Programming Author Index Key Word Index

巻冊次

3 ISBN 9781558603561

内容説明

Foundations of Genetic Algorithms, 3 focuses on the principles, methodologies, and approaches involved in the integration of genetic algorithm into mainstream mathematics, as well as genetic operators, genetic programming, and evolutionary algorithms. The selection first offers information on an experimental design perspective on genetic algorithms; schema theorem and price's theorem; and fitness variance of formae and performance prediction. Discussions focus on representation-independent recombination, representation-independent mutation and hill-climbing, recombination and the re-emergence of schemata, and Walsh transforms and deception. The publication then examines the troubling aspects of a building block hypothesis for genetic programming and order statistics for convergence velocity analysis of simplified evolutionary algorithms. The manuscript ponders on stability of vertex fixed points and applications; predictive models using fitness distributions of genetic operators; and modeling simple genetic algorithms for permutation problems. Topics include exact models for permutations, fitness distributions of genetic operators, predictive model based on linear fitness distributions, and stability in the simplex. The book also takes a look at the role of development in genetic algorithms and productive recombination and propagating and preserving schemata. The selection is a dependable source of data for mathematicians and researchers interested in genetic algorithms.

目次

?Introduction Part 1: Schema Based Analyses An Experimental Design Perspective on Genetic Algorithms The Schema Theorem and Price's Theorem Fitness Variance of Formae and Performance Prediction The Troubling Aspects of a Building Block Hypothesis for Genetic Programming Part 2: Convergence and Predictive Models Order Statistics for Convergence Velocity Analysis of Simplified Evolutionary Algorithms Stability of Vertex Fixed Points and Applications Using Markov Chains to Analyze GAFOs Predictive Models Using Fitness Distributions of Genetic Operators Modeling Simple Genetic Algorithms for Permutation Problems Population Size and Genetic Drift in Fitness Sharing An Approach to the Study of Sensitivity for a Class of Genetic Algorithms Part 3: Fitness Landscapes and Genetic Operators Genetic Algorithm Difficulty and the Modality of Fitness Landscapes Greedy Recombination and Genetic Search on the Space of Computer Programs Productive Recombination and Propagating and Preserving Schemata The Role of Development in Genetic Algorithms Author Index Key Word Index

巻冊次

5 ISBN 9781558605596

内容説明

Foundations of Genetic Algorithms, Volume 5 is the fifth in the series of books recording the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organized by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. This volume's papers deal with GA dynamics; genetic operators (mostly in their relationship to schemata); characterization of landscapes over which an algorithm is searching; and the interaction between different parameters or strategies used for controlling the course of genetic search.

巻冊次

6 ISBN 9781558607347

内容説明

Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones.

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA12941531
• ISBN:
  • 1558601708
  • 1558602631
  • 1558603565
  • 155860460X
  • 1558605592
  • 155860734X
  • 0122081552
• LCCN: 91053076
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: San Mateo, Calif.
• ページ数/冊数: v.
• 大きさ: 22-24 cm
• 分類:
  • NDC8 : 007.64
• 件名:
  • LCSH : Algorithms