Foundations of Genetic Algorithms 1995 (FOGA 3) - 1st Edition - ISBN: 9781558603561, 9781483295022

Foundations of Genetic Algorithms 1995 (FOGA 3), Volume 3

1st Edition

Editors: FOGA
eBook ISBN: 9781483295022
Imprint: Morgan Kaufmann
Published Date: 1st June 1995
Page Count: 300
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Table of Contents


Part 1: Schema Based Analyses

An Experimental Design Perspective on Genetic Algorithms

The Schema Theorem and Price's Theorem

Fitness Variance of Formae and Performance Prediction

The Troubling Aspects of a Building Block Hypothesis for Genetic Programming

Part 2: Convergence and Predictive Models

Order Statistics for Convergence Velocity Analysis of Simplified Evolutionary Algorithms

Stability of Vertex Fixed Points and Applications

Using Markov Chains to Analyze GAFOs

Predictive Models Using Fitness Distributions of Genetic Operators

Modeling Simple Genetic Algorithms for Permutation Problems

Population Size and Genetic Drift in Fitness Sharing

An Approach to the Study of Sensitivity for a Class of Genetic Algorithms

Part 3: Fitness Landscapes and Genetic Operators

Genetic Algorithm Difficulty and the Modality of Fitness Landscapes

Greedy Recombination and Genetic Search on the Space of Computer Programs

Productive Recombination and Propagating and Preserving Schemata

The Role of Development in Genetic Algorithms

Author Index

Key Word Index


Foundations of Genetic Algorithms, 3 focuses on the principles, methodologies, and approaches involved in the integration of genetic algorithm into mainstream mathematics, as well as genetic operators, genetic programming, and evolutionary algorithms.

The selection first offers information on an experimental design perspective on genetic algorithms; schema theorem and price's theorem; and fitness variance of formae and performance prediction. Discussions focus on representation-independent recombination, representation-independent mutation and hill-climbing, recombination and the re-emergence of schemata, and Walsh transforms and deception. The publication then examines the troubling aspects of a building block hypothesis for genetic programming and order statistics for convergence velocity analysis of simplified evolutionary algorithms.

The manuscript ponders on stability of vertex fixed points and applications; predictive models using fitness distributions of genetic operators; and modeling simple genetic algorithms for permutation problems. Topics include exact models for permutations, fitness distributions of genetic operators, predictive model based on linear fitness distributions, and stability in the simplex. The book also takes a look at the role of development in genetic algorithms and productive recombination and propagating and preserving schemata.

The selection is a dependable source of data for mathematicians and researchers interested in genetic algorithms.


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© Morgan Kaufmann 1995
Morgan Kaufmann
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