Evolutionary Optimization Algorithms

<p><b>A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms</b> <p>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. <p>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. <p><i>Evolutionary Optimization Algorithms:</i> <ul> <li>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</li> <li>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</li> <li>Includes chapter-end problems plus a solutions manual available online for instructors</li> <li>Offers simple examples that provide the reader with an intuitive understanding of the theory</li> <li>Features source code for the examples available on the author’s website</li> <li>Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling</li> </ul> <p>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.