Evolutionary Optimization Algorithms

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms

Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies.

This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others.

Evolutionary Optimization Algorithms:

• Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear—but theoretically rigorous—understanding of evolutionary algorithms, with an emphasis on implementation
• Gives a careful treatment of recently developed EAs—including opposition-based learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more well-established EAs
• Includes chapter-end problems plus a solutions manual available online for instructors
• Offers simple examples that provide the reader with an intuitive understanding of the theory
• Features source code for the examples available on the author’s website
• Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling

Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Table of Contents

Part I Introduction To Evolutionary Optimization
Chapter 1 Introduction
Chapter 2 Optimization

Part II Classic Evolutionary Algorithms
Chapter 3 Genetic Algorithms
Chapter 4 Mathematical Models Of Genetic Algorithms
Chapter 5 Evolutionary Programming
Chapter 6 Evolution Strategies
Chapter 7 Genetic Programming
Chapter 8 Evolutionary Algorithm Variations

Part III More Recent Evolutionary Algorithms
Chapter 9 Simulated Annealing
Chapter 10 Ant Colony Optimization
Chapter 11 Particle Swarm Optimization
Chapter 12 Differential Evolution
Chapter 13 Estimation Of Distribution Algorithms
Chapter 14 Biogeography-Based Optimization
Chapter 15 Cultural Algorithms
Chapter 16 Opposition-Based Learning
Chapter 17 Other Evolutionary Algorithms

Part IV Special Types Of Optimization Problems
Chapter 18 Combinatorial Optimization
Chapter 19 Constrained Optimization
Chapter 20 Multi-Objective Optimization
Chapter 21 Expensive, Noisy, And Dynamic Fitness Functions

Part V Appendices
Appendix A: Some Practical Advice
Appendix B: The No Free Lunch Theorem And Performance Testing
Appendix C: Benchmark Optimization Functions