Foundations of Genetic Algorithms 2003 (FOGA 7)

Name: Foundations of Genetic Algorithms 2003 (FOGA 7)
Price: 33.53 USD
Availability: InStock
ISBN: 9780122081552

ISBN-10: 0122081552

ISBN-13: 9780122081552

Edition: 2003

Authors: Kenneth A. De Jong, Riccardo Poli, Jonathan Rowe

List price: $96.95

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Book details

List price: $96.95
Copyright year: 2003
Publisher: Elsevier Science & Technology
Publication date: 10/2/2003
Binding: Paperback
Pages: 416
Size: 7.00" wide x 9.00" long x 1.00" tall
Weight: 2.2
Language: English

Jonathan Rowe was a Nader's "Raider," a US Senate aide, an editor at the Washington Monthly, and cofounder of OntheCommons.org.



Editorial Introduction


Schema Analysis of One


Max 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 ï¿½ï¿½Hamming Landscapeï¿½ 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 vyznuw{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