This volume discusses the basic ideas which underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, genetic programming.

• 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.