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Foundations of genetic algorithms 6
Title:
Foundations of genetic algorithms 6
Author:
Martin, W. N. (Worthy N.)
ISBN:
9781558607347
9780080506876
Publication Information:
San Francisco, Calif. : Morgan Kaufmann, ©2001.
Physical Description:
1 online resource (342 pages) : illustrations.
Series:
The Morgan Kaufmann series in evolutionary computation,
Morgan Kaufmann series in evolutionary computation.
General Note:
"The 2000 Foundations of Genetic Algorithms (FOGA-6) workshop was the sixth biennial meeting in this series of workshops"--Page 1.
Contents:
Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents; Chapter 1. Introduction; Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces; Chapter 3. Niches in NK-Landscapes; Chapter 4. New Methods for Tunable, Random Landscapes; Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem; Chapter 6. Direct Statistical Estimation of GA Landscape Properties; Chapter 7. Comparing Population Mean Curves; Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment
Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic AlgorithmsChapter 10. Towards a Theory of Strong Overgeneral Classifiers; Chapter 11. Evolutionary Optimization through PAC Learning; Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms; Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach; Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination; Chapter 15. The Mixing Rate of Different Crossover Operators; Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms
Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and NeighborhoodsChapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index
Abstract:
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.
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Electronic Access:
ScienceDirect http://www.sciencedirect.com/science/book/9781558607347 ScienceDirect https://www.sciencedirect.com/science/book/9781558607347Available:*
Shelf Number | Item Barcode | Shelf Location | Status |
|---|---|---|---|
| QA402.5 .F686 2001 EB | 1188155-1001 | Elsevier E-Book Collections | Searching... |
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