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Foundations of Genetic Algorithms 6 (FOGA 6) : Foundations of Genetic Algorithms - Worth Martin

Foundations of Genetic Algorithms 6 (FOGA 6)

Foundations of Genetic Algorithms

Hardcover

Published: 1st June 2001
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Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems.

Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones.

  • Includes research from academia, government laboratories, and industry
  • Contains high calibre papers which have been extensively reviewed
  • Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field
  • Ideal for researchers in machine learning, specifically those involved with evolutionary computation

Introductionp. 1
Overcoming Fitness Barriers in Multi-Modal Search Spacesp. 5
Niches in NK-Landscapesp. 27
New Methods for Tunable, Random Landscapesp. 47
Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problemp. 69
Direct Statistical Estimation of GA Landscape Propertiesp. 91
Comparing Population Mean Curvesp. 109
Local Performance of the ((/(I, () -ES in a Noisy Environmentp. 127
Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithmsp. 143
Towards a Theory of Strong Overgeneral Classifiersp. 165
Evolutionary Optimization through PAC Learningp. 185
Continuous Dynamical System Models of Steady-State Genetic Algorithmsp. 209
Mutation-Selection Algorithm: A Large Deviation Approachp. 227
The Equilibrium and Transient Behavior of Mutation and Recombinationp. 241
The Mixing Rate of Different Crossover Operatorsp. 261
Dynamic Parameter Control in Simple Evolutionary Algorithmsp. 275
Local Search and High Precision Gray Codes: Convergence Results and Neighborhoodsp. 295
Burden and Benefits of Redundancyp. 313
Author Indexp. 335
Key Word Indexp. 337
Table of Contents provided by Rittenhouse. All Rights Reserved.

ISBN: 9781558607347
ISBN-10: 155860734X
Series: Foundations of Genetic Algorithms
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 342
Published: 1st June 2001
Country of Publication: US
Dimensions (cm): 23.67 x 18.29  x 2.31
Weight (kg): 0.69