{"product_id":"evolutionary-optimization-algorithms-isbn-9780470937419","title":"Evolutionary Optimization Algorithms","description":"\u003cp\u003e\u003cb\u003eA clear and lucid bottom-up approach to the basic principles of evolutionary algorithms\u003c\/b\u003e \t \u003c\/p\u003e\u003cp\u003eEvolutionary 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. \u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eEvolutionary Optimization Algorithms:\u003c\/i\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides 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\u003c\/li\u003e \u003cli\u003eGives 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\u003c\/li\u003e \u003cli\u003eIncludes chapter-end problems plus a solutions manual available online for instructors\u003c\/li\u003e \u003cli\u003eOffers simple examples that provide the reader with an intuitive understanding of the theory\u003c\/li\u003e \u003cli\u003eFeatures source code for the examples available on the author's website\u003c\/li\u003e \u003cli\u003eProvides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEvolutionary 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. \u003c\/p\u003e\u003cp\u003eAcknowledgments xxi\u003cbr\u003e \u003cbr\u003e Acronyms xxiii\u003cbr\u003e \u003cbr\u003e List of Algorithms xxvii\u003cbr\u003e \u003cbr\u003e \u003cb\u003ePart I: Introduction to Evolutionary Optimization\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 1 Introduction 1\u003cbr\u003e \u003cbr\u003e 2 Optimization 11\u003cbr\u003e \u003cbr\u003e \u003cb\u003ePart II: Classic Evolutionary Algorithms\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 3 Generic Algorithms 35\u003cbr\u003e \u003cbr\u003e 4 Mathematical Models of Genetic Algorithms 63\u003cbr\u003e \u003cbr\u003e 5 Evolutionary Programming 95\u003cbr\u003e \u003cbr\u003e 6 Evolution Strategies 117\u003cbr\u003e \u003cbr\u003e 7 Genetic Programming 141\u003cbr\u003e \u003cbr\u003e 8 Evolutionary Algorithms Variations 179\u003cbr\u003e \u003cbr\u003e \u003cb\u003ePart III: More Recent Evolutionary Algorithms\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 9 Simulated Annealing 223\u003cbr\u003e \u003cbr\u003e 10 Ant Colony Optimization 241\u003cbr\u003e \u003cbr\u003e 11 Particle Swarm Optimization 265\u003cbr\u003e \u003cbr\u003e 12 Differential Evolution 293\u003cbr\u003e \u003cbr\u003e 13 Estimation of Distribution Algorithms 313\u003cbr\u003e \u003cbr\u003e 14 Biogeography-Based Optimization 351\u003cbr\u003e \u003cbr\u003e 15 Cultural Algorithms 377\u003cbr\u003e \u003cbr\u003e 16 Opposition-Based Learning 397\u003cbr\u003e \u003cbr\u003e 17 Other Evolutionary Algorithms 421\u003cbr\u003e \u003cbr\u003e \u003cb\u003ePart IV: Special Type of Optimization Problems\u003c\/b\u003e \u003cbr\u003e \u003cbr\u003e 18 Combinatorial Optimization 449\u003cbr\u003e \u003cbr\u003e 19 Constrained Optimization 481\u003cbr\u003e \u003cbr\u003e 20 Multi-Objective Optimization 517\u003cbr\u003e \u003cbr\u003e 21 Expensive, Noisy and Dynamic Fitness Functions 563\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAppendices\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e A Some Practical Advice 607\u003cbr\u003e \u003cbr\u003e B The No Free Lunch Theorem and Performance Testing 613\u003cbr\u003e \u003cbr\u003e C Benchmark Optimization Functions 641\u003c\/p\u003e \u003cp\u003eReferences 685\u003c\/p\u003e \u003cp\u003eTopic Index 727\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003csmall\u003eDAN SIMON \u003c\/small\u003e\u003c\/b\u003eis a Professor at Cleveland State University in the Department of Electrical and Computer Engineering. His teaching and research interests include control theory, computer intelligence, embedded systems, technical writing, and related subjects. He is the author of the book Optimal State Estimation (Wiley).    \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA clear and lucid bottom-up approach to the basic principles of evolutionary algorithms\u003c\/b\u003e \t \u003c\/p\u003e\u003cp\u003eEvolutionary 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. \u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eEvolutionary Optimization Algorithms:\u003c\/i\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides 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\u003c\/li\u003e \u003cli\u003eGives 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\u003c\/li\u003e \u003cli\u003eIncludes chapter-end problems plus a solutions manual available online for instructors\u003c\/li\u003e \u003cli\u003eOffers simple examples that provide the reader with an intuitive understanding of the theory\u003c\/li\u003e \u003cli\u003eFeatures source code for the examples available on the author's website\u003c\/li\u003e \u003cli\u003eProvides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEvolutionary 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989179646181,"sku":"NP9780470937419","price":142.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470937419.jpg?v=1761783110","url":"https:\/\/k12savings.com\/es\/products\/evolutionary-optimization-algorithms-isbn-9780470937419","provider":"K12savings","version":"1.0","type":"link"}