Evolutionary Computation 1

Zusatztext "This new volume contains some extended material and provides basic information on evolutionary algorithms ? This book provides an interesting reference for theorists! teachers! and practitioners also."-H.D. Hecker! Zentralblatt Math Informationen zum Autor Thomas Baeck, D.B Fogel, Z Michalewicz Klappentext The field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field. Zusammenfassung Offers information on algorithms and operators used in evolutionary computing. This book discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is suitable for individual researchers, teachers, and students in the field. Inhaltsverzeichnis PART 1 WHY EVOLUTIONARY COMPUTATION? 1. Introduction to evolutionary computation. 2. Possible applications of evolutationary computation. 3. Advantages (and disadvantages) of evolutionary computation over other approaches. PART 2. EVOLUTIONARY COMPUTATION: THE BACKGROUND. 4. Principles of evolutionary processes. 5. Principles of genetics. 6. A history of evolutionary computation. PART 3 EVOLUTIONARY ALGORITHMS AND THEIR STANDARD INSTANCES. 7. Introduction to evolutionary algorithms. 8. Genetic algorithms. 9. Evolution strategies. 10. Evolutionary programming. 11. Derivative methods in genetic programming. 12. Learning classifier systems. 13. Hybrid methods. PART 4. REPRESENTATIONS. 14. Introduction to representations. 15. Binary strings. 16. Real-valued vectors. 17. Permutations. 18. Finite-state representations. 19. Parse trees. 20. Guidelines for a suitable encoding. 21. Other representations. PART 5. SELECTION. 22. Introduction to selection. 23. Proportionary selection and sampling algorithms. 24. Tournament selection. 25. Rank-based selection. 26. Boltzmann selection. 27. Other selection methods. 28. Generation gap methods. 29. A comparison of selection mechanisms. 30. Interactive evolution. PART 6. SEARCH OPERATORS. 31. Introduction to search operators. 32. Mutation operators. 33. Recombination. 34. Other operators. Index....

Autorentext
Thomas Baeck, D.B Fogel, Z Michalewicz

Klappentext
The field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field.

Zusammenfassung
Offers information on algorithms and operators used in evolutionary computing. This book discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is suitable for individual researchers, teachers, and students in the field.

Inhalt
PART 1 WHY EVOLUTIONARY COMPUTATION? 1. Introduction to evolutionary computation. 2. Possible applications of evolutationary computation. 3. Advantages (and disadvantages) of evolutionary computation over other approaches. PART 2. EVOLUTIONARY COMPUTATION: THE BACKGROUND. 4. Principles of evolutionary processes. 5. Principles of genetics. 6. A history of evolutionary computation. PART 3 EVOLUTIONARY ALGORITHMS AND THEIR STANDARD INSTANCES. 7. Introduction to evolutionary algorithms. 8. Genetic algorithms. 9. Evolution strategies. 10. Evolutionary programming. 11. Derivative methods in genetic programming. 12. Learning classifier systems. 13. Hybrid methods. PART 4. REPRESENTATIONS. 14. Introduction to representations. 15. Binary strings. 16. Real-valued vectors. 17. Permutations. 18. Finite-state representations. 19. Parse trees. 20. Guidelines for a suitable encoding. 21. Other representations. PART 5. SELECTION. 22. Introduction to selection. 23. Proportionary selection and sampling algorithms. 24. Tournament selection. 25. Rank-based selection. 26. Boltzmann selection. 27. Other selection methods. 28. Generation gap methods. 29. A comparison of selection mechanisms. 30. Interactive evolution. PART 6. SEARCH OPERATORS. 31. Introduction to search operators. 32. Mutation operators. 33. Recombination. 34. Other operators. Index.

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