Foundations of Genetic Algorithms 6 (FOGA-6) (The Morgan Kaufmann Series in Artificial Intelligence)

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 industryContains high calibre papers which have been extensively reviewedContinues the tradition of presenting not only current theoretical work but also issues that could shape future research in the fieldIdeal for researchers in machine learning, specifically those involved with evolutionary computation Machine generated contents note: Introduction Worthy N. Martin and William M. Spears Overcoming Fitness Barriers in Multi-Modal Search Spaces5 Martin J. Oates and David Come N iches in N K -Landscapes27 Keith E. Mathia, Larry J. Eshelman, and J. David Schaffer New Methods for Tunable, Random Landscapes 47 R. E. Smith and J. E. Smith Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem69 Richard A. Watson Direct Statistical Estimation of GA Landscape Properties 91 Colin R. Reeves Comparing Population Mean Curves109 B. Naudts and I. Landrieu Local Performance of the ((/(I, () -ES in a Noisy Environment 127 Dirk V Arnold and Hans-Georg Beyer Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithms 143 Riccardo Poli Towards a Theory of Strong Overgeneral Classifiers 165 Tim Kovacs Evolutionary Optimization through PAC Learning 185 Forbes J. Burkowski Continuous Dynamical System Models of Steady-State Genetic Algorithms209 Alden H. Wright and Jonathan E. Rowe Mutation-Selection Algorithm: A Large Deviation Approach 227 Paul Albuquerque and Christian Mazza The Equilibrium and Transient Behavior of Mutation and Recombination 241 William M. Spears The Mixing Rate of Different Crossover Operators 261 Adam Prigel-Bennett Dynamic Parameter Control in Simple Evolutionary Algorithms 275 Stefan Droste, Thomas Jansen, and Ingo Wegener Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods295 Darrell Whitley, Laura Barbulescu, and Jean-Paul Watson Burden and Benefits of Redundancy 313 Karsten Weicker and Nicole Weicker Author Index 335 Key Word Index337. 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 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 In order to test the suitability of an evolutionary algorithm designed for real-world application, thorough parameter testing is needed to establish parameter sensitivity, solution quality reliability, and associated issues. Papers presented at the 2000 Foundations of Genetic Algorithms (FOGA-6) sixth biennial workshop held in Charlottesville, VA