Evolutionary Optimization Algorithms: Biologically-Inspired and Population-Based Approaches to Computer Intelligence

✍ Scribed by Simon D.

• Tongue: English
• Leaves: 776
• Category: Library

✦ Synopsis

Wiley, 2013. — 776 p. — ISBN: 0470937416, 9780470937419

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms
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.
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.
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 engineering and computer science.

Contents:
Acknowledgments
Acronyms
List of Algorithms
Introduction to Evolutionary Optimization
Introduction
Optimization
Classic Evoluntionary Algorithms
Generic Algorithms
Mathematical Models of Genetic Algorithms
Evolutionary Programming
Evolution Strategies
Genetic Programming
Evolutionary Algorithms Variations
More Recent Evolutionary Algorithms
Simulated Annealing
Ant Colony Optimization
Particle Swarm Optimization
Differential Evolution
Estimation of Distribution Algorithms
Biogeography-Based Optimization
Cultural Algorithms
Oppostion-Based Learning
Other Evolutionary Algorithms
Special Type of Optimization Problems
Combinatorial Optimization
Constrained Optimization
Multi-Objective Optimization
Expensive, Noisy and Dynamic Fitness Functions
Appendices
A Some Practical Advice
B The No Free Luch Therorem and Performance Testing
C Benchmark Optimization Functions

✦ Subjects

Информатика и вычислительная техника;Искусственный интеллект;Эволюционные алгоритмы