Evolutionary Optimization Algorithms

(1 Customer Reviews)
, 2013-04-22, 772 pages

Editorial Reviews

A clear and lucid bottom-up approach to the basic of evolutionary

Evolutionary algorithms (EAs) are a type of . 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 , 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.

Evolutionary Optimization Algorithms:

  • 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
  • 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
  • Includes chapter-end 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 Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in and computer science.

Table of Contents

Part I Introduction To Evolutionary Optimization
Chapter 1 Introduction
Chapter 2 Optimization

Part II Classic Evolutionary Algorithms
Chapter 3 Genetic Algorithms
Chapter 4 Mathematical Models Of Genetic Algorithms
Chapter 5 Evolutionary Programming
Chapter 6 Evolution Strategies
Chapter 7 Genetic Programming
Chapter 8 Evolutionary Variations

Part III More Recent Evolutionary Algorithms
Chapter 9 Simulated Annealing
Chapter 10 Ant Colony Optimization
Chapter 11 Particle Swarm Optimization
Chapter 12 Differential Evolution
Chapter 13 Estimation Of Distribution Algorithms
Chapter 14 Biogeography-Based Optimization
Chapter 15 Cultural Algorithms
Chapter 16 Opposition-Based Learning
Chapter 17 Other Evolutionary Algorithms

Part IV Special Types Of Optimization Problems
Chapter 18 Combinatorial Optimization
Chapter 19 Constrained Optimization
Chapter 20 Multi-Objective Optimization
Chapter 21 Expensive, Noisy, And Dynamic Fitness Functions

Part V Appendices
Appendix A: Some Practical Advice
Appendix B: The No Free Lunch Theorem And Performance Testing
Appendix C: Benchmark Optimization Functions

Book Details

QR code for Evolutionary Optimization Algorithms
  • Title: Evolutionary Optimization Algorithms
  • Author:
  • Length: 772 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2013-04-22
  • ISBN-10: 0470937416
  • ISBN-13: 9780470937419
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