A clear and lucid bottom-up approach to the basic principlesof evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificialintelligence. EAs are motivated by optimization processes that weobserve in nature, such as natural selection, species migration,bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, andprogramming of evolutionary optimization algorithms. Featuredalgorithms include genetic algorithms, genetic programming, antcolony optimization, particle swarm optimization, differentialevolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists thereader in obtaining a clear—but theoreticallyrigorous—understanding of evolutionary algorithms, with anemphasis on implementation Gives a careful treatment of recently developedEAs—including opposition-based learning, artificial fishswarms, bacterial foraging, and many others— and discussestheir similarities and differences from more well-establishedEAs Includes chapter-end problems plus a solutions manual availableonline for instructors Offers simple examples that provide the reader with anintuitive understanding of the theory Features source code for the examples available on the author'swebsite Provides advanced mathematical techniques for analyzing EAs,including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspiredand Population-Based Approaches to Computer Intelligence is anideal text for advanced undergraduate students, graduate students,and professionals involved in engineering and computer science.