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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Maße
Höhe: 229 mm
Breite: 178 mm
Gewicht
ISBN-13
978-0-12-208155-2 (9780122081552)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Klassifikation
Edited by Kenneth A. De Jong, Riccardo Poli and Jonathan Rowe
Herausgeber*in
George Mason University
University of Essex, UK
University of Birmingham, UK
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 ?v?Y? (1)?z?n?UE?w?{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
