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
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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