遺伝アルゴリズムの基礎:FOGA7<br>Foundations of Genetic Algorithms 7

遺伝アルゴリズムの基礎:FOGA7
Foundations of Genetic Algorithms 7

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  • 製本 Hardcover:ハードカバー版/ページ数 405 p.
  • 言語 ENG
  • 商品コード 9780122081552
  • DDC分類 519.7

基本説明

A collection of 22 papers written by the field's leading researchers. Much more than proceedings, this book and its companion six volumes document the bi-annual FOGA workshops.

Full Description


"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. It documents the bi-annual FOGA workshop that occurred in 2001.

Contents

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 'Hamming 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 vY'znUw{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