Thomas Baeck, D.B Fogel, Z Michalewicz

"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

• 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.

Thomas Baeck, D.B Fogel, Z Michalewicz

"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

• 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.

Thomas Baeck, D.B Fogel, Z Michalewicz

"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

• 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.