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Part 1: Genetic Algorithm Hardness
The Nonuniform Walsh-Schema Transform
Epistasis Variance: A Viewpoint on GA-Hardness
Deceptiveness and Genetic Algorithm Dynamics
Part 2: Selection and Convergence
An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
A Study of Reproduction in Generational and Steady State Genetic Algorithms
Spurious Correlations and Premature Convergence in Genetic Algorithms
Part 3: Classifier Systems
Representing Attribute-Based Concepts in a Classifier System
Quasimorphisms or Queasymorphisms? Modeling Finite Automaton Environments
Variable Default Hierarchy Separation in a Classifier System
Part 4: Coding and Representation
A Hierarchical Approach to Learning the Boolean Multiplexer Function
A Grammar-Based Genetic Algorithm
Genetic Algorithms for Real Parameter Optimization
Part 5: Framework Issues
Fundamental Principles of Deception in Genetic Search
Isomorphisms of Genetic Algorithms
Conditions for Implicit Parallelism
Part 6: Variation and Recombination
The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination
Genetic Operators for Sequencing Problems
An Analysis of Multi-Point Crossover
Evolution in Time and Space—The Parallel Genetic Algorithm
Key Word Index
Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems.
This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; conditions for implicit parallelism; and analysis of multi-point crossover are also elaborated. This text likewise covers the genetic algorithms for real parameter optimization and isomorphisms of genetic algorithms. This publication is a good reference for students and researchers interested in genetic algorithms.
- No. of pages:
- © Morgan Kaufmann 2014
- 1st July 1991
- Morgan Kaufmann
- Hardcover ISBN:
- Paperback ISBN:
- eBook ISBN:
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