Foundations of global genetic optimization /

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Bibliographic Details
Author / Creator:	Schaefer, Robert.
Imprint:	Berlin ; New York : Springer, ©2007.
Description:	1 online resource (x, 222 pages) : illustrations.
Language:	English
Series:	Studies in computational intelligence, 1860-949X ; v. 74 Studies in computational intelligence ; v. 74.
Subject:	Genetic algorithms -- Mathematical models. Genetic algorithms -- Data processing. Evolutionary computation. Combinatorial optimization. Evolutionary programming (Computer science) Genetic algorithms. Mathematical optimization.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11066182

Hidden Bibliographic Details
Other authors / contributors:	Telega, Henryk.
ISBN:	9783540731924 354073192X 9783540731917 3540731911
Notes:	Includes bibliographical references (pages 207-217) and index. Print version record.
Summary:	"This book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations."--Jacket.
Other form:	Print version: Schaefer, Robert. Foundations of global genetic optimization. Berlin ; New York : Springer, ©2007 9783540731917 3540731911

Table of Contents:

Cover
Contents
1 Introduction
2 Global optimization problems
2.1 Definitions of global optimization problems
2.2 General schema of a stochastic search
2.3 Basic features of stochastic algorithms of global optimization
2.4 Genetic algorithms in action
solution of inverse problems in the mechanics of continua
3 Basic models of genetic computations
3.1 Encoding and inverse encoding
3.1.1 Binary affine encoding
3.1.2 Gray encoding
3.1.3 Phenotypic encoding
3.2 Objective and fitness
3.3 The individual and population models
3.4 Selection
3.4.1 Proportional (roulette) selection
3.4.2 Tournament selection
3.4.3 Elitist selection
3.4.4 Rank selection
3.5 Binary genetic operations
3.5.1 Multi-point mutation
3.5.2 Binary crossover
3.5.3 Features of binary genetic operations, mixing
3.6 Definition of the Simple Genetic Algorithm (SGA)
3.7 Phenotypic genetic operations
3.7.1 Phenotypic mutation
3.7.2 Phenotypic crossover
3.7.3 Phenotypic operations in constrained domains
3.8 Schemes for creating a new generation
3.9,
taxonomy of single- and multi-deme strategies
4 Asymptotic behavior of the artificial genetic systems
4.1 Markov theory of genetic algorithms
4.1.1 Markov chains in genetic algorithm asymptotic analysis
4.1.2 Markov theory of the Simple Genetic Algorithm
4.1.3 The results of the Markov theory for Evolutionary Algorithm
4.2 Asymptotic results for very small populations
4.2.1 The rate of convergence of the single individual population with hard succession
4.2.2 The dynamics of double individual populations with proportional selection
4.3 The increment of the schemata cardinality in the single evolution epoch
4.4 Summary of practicals coming from asymptotic theory
5 Adaptation in genetic search
5.1 Adaptation and self-adaptation in genetic search
5.2 The taxonomy of adaptive genetic strategies
5.3 Single- and twin-population strategies ()
5.3.1 Adaptation of genetic operation parameters (.1)
5.3.2 Strategies with a variable life time of individuals (.2)
5.3.3 Selection of the operation from the operation set (.3)
5.3.4 Introducing local optimization methods to the evolution (.4)
5.3.5 Fitness modification (.5)
5.3.6 Additional replacement of individuals (.6)
5.3.7 Speciation (.7)
5.3.8 Variable accuracy searches (.8)
5.4 Multi-deme strategies ()
5.4.1 Metaevolution (.1)
5.4.2 Island models (.2)
5.4.3 Hierarchic Genetic Strategies (.3)
5.4.4 Inductive Genetic Programming (iGP) (.4)
6 Two-phase stochastic global optimization strategies
6.1 Overview of.

Foundations of global genetic optimization /

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