A clear and lucid bottomup 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, biogeographybased optimization, and many others.
Evolutionary Optimization Algorithms:

Provides a straightforward, bottomup 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 oppositionbased learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more wellestablished EAs

Includes chapterend 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 PopulationBased Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.