+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Foundations of Genetic Algorithms 1991 (FOGA 1), Vol 1

  • ID: 3692775
  • Book
  • September 1991
  • 341 Pages
  • Elsevier Science and Technology
1 of 3

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.

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Note: Product cover images may vary from those shown
2 of 3
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

Author Index

Key Word Index
Note: Product cover images may vary from those shown
3 of 3
Note: Product cover images may vary from those shown