Foundations of Genetic Algorithms 1993 (FOGA 2) - 1st Edition

Foundations of Genetic Algorithms 1993 (FOGA 2), Volume 2

1st Edition

Editors: FOGA
Imprint: Morgan Kaufmann
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Table of Contents


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


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.


Morgan Kaufmann
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