Evolutionary optimization algorithms /
"This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. Covering the theory, history, mathematics, and applications of evolutionary optimization algorithms, thi...
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Full text (MCPHS users only) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Chichester :
Wiley-Blackwell,
2013
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
MARC
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| 100 | 1 | |a Simon, Dan, |d 1960- |e author. |1 https://id.oclc.org/worldcat/entity/E39PBJj8VqGX7k4hbBGMKcT8md | |
| 245 | 1 | 0 | |a Evolutionary optimization algorithms / |c Dan Simon. |
| 264 | 1 | |a Chichester : |b Wiley-Blackwell, |c 2013. | |
| 300 | |a 1 online resource (1 volume) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover; Title Page; Copyright Page; SHORT TABLE OF CONTENTS; DETAILED TABLE OF CONTENTS; Acknowledgments; Acronyms; List of Algorithms; PART I INTRODUCTION TO EVOLUTIONARY OPTIMIZATION; 1 Introduction; 1.1 Terminology; 1.2 Why Another Book on Evolutionary Algorithms?; 1.3 Prerequisites; 1.4 Homework Problems; 1.5 Notation; 1.6 Outline of the Book; 1.7 A Course Based on This Book; 2 Optimization; 2.1 Unconstrained Optimization; 2.2 Constrained Optimization; 2.3 Multi-Objective Optimization; 2.4 Multimodal Optimization; 2.5 Combinatorial Optimization; 2.6 Hill Climbing. | |
| 505 | 8 | |a 2.6.1 Biased Optimization Algorithms2.6.2 The Importance of Monte Carlo Simulations; 2.7 Intelligence; 2.7.1 Adaptation; 2.7.2 Randomness; 2.7.3 Communication; 2.7.4 Feedback; 2.7.5 Exploration and Exploitation; 2.8 Conclusion; Problems; PART II CLASSIC EVOLUTIONARY ALGORITHMS; 3 Genetic Algorithms; 3.1 The History of Genetics; 3.1.1 Charles Darwin; 3.1.2 Gregor Mendel; 3.2 The Science of Genetics; 3.3 The History of Genetic Algorithms; 3.4 A Simple Binary Genetic Algorithm; 3.4.1 A Genetic Algorithm for Robot Design; 3.4.2 Selection and Crossover; 3.4.3 Mutation; 3.4.4 GA Summary. | |
| 505 | 8 | |a 3.4.5 GA Tuning Parameters and Examples3.5 A Simple Continuous Genetic Algorithm; 3.6 Conclusion; Problems; 4 Mathematical Models of Genetic Algorithms; 4.1 Schema Theory; 4.2 Markov Chains; 4.3 Markov Model Notation for Evolutionary Algorithms; 4.4 Markov Models of Genetic Algorithms; 4.4.1 Selection; 4.4.2 Mutation; 4.4.3 Crossover; 4.5 Dynamic System Models of Genetic Algorithms; 4.5.1 Selection; 4.5.2 Mutation; 4.5.3 Crossover; 4.6 Conclusion; Problems; 5 Evolutionary Programming; 5.1 Continuous Evolutionary Programming; 5.2 Finite State Machine Optimization. | |
| 505 | 8 | |a 7.2.6 Genetic Programming Parameters7.3 Genetic Programming for Minimum Time Control; 7.4 Genetic Programming Bloat; 7.5 Evolving Entities other than Computer Programs; 7.6 Mathematical Analysis of Genetic Programming; 7.6.1 Definitions and Notation; 7.6.2 Selection and Crossover; 7.6.3 Mutation and Final Results; 7.7 Conclusion; Problems; 8 Evolutionary Algorithm Variations; 8.1 Initialization; 8.2 Convergence Criteria; 8.3 Problem Representation Using Gray Coding; 8.4 Elitism; 8.5 Steady-State and Generational Algorithms; 8.6 Population Diversity; 8.6.1 Duplicate Individuals. | |
| 520 | |a "This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. Covering the theory, history, mathematics, and applications of evolutionary optimization algorithms, this timely and practical book offers lengthy examples, a companion website, MATLAB code, and a Solutions Manual--making it perfect for advanced undergraduates, graduates, and practicing engineers involved in engineering and computer science"-- |c Provided by publisher. | ||
| 520 | |a "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 rather than models"-- |c Provided by publisher. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Evolutionary computation. | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 0 | |a Natural computation. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 7 | |a algorithms. |2 aat | |
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