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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 clearbut theoretically rigorousunderstanding of evolutionary algorithms, with an emphasis on implementation
- Gives a careful treatment of recently developed EAsincluding 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.
DAN SIMON is 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).
Table of Contents
1 Introduction 1
2 Optimization 11
Part II: Classic Evoluntionary Algorithms
3 Generic Algorithms 35
4 Mathematical Models of Genetic Algorithms 63
5 Evolutionary Programming 95
6 Evolution Strategies 117
7 Genetic Programming 141
8 Evolutionary Algorithms Variations 179
Part III: More Recent Evolutionary Algorithms
9 Simulated Annealing 223
10 Ant Colony Optimization 241
11 Particle Swarm Optimization 265
12 Differential Evolution 293
13 Estimation of Distribution Algorithms 313
14 Biogeography-Based Optimization 351
15 Cultural Algorithms 377
16 Oppostion-Based Learning 397
17 Other Evolutionary Algorithms 421
Part IV: Special Type of Optimization Problems
18 Combinatorial Optimization 449
19 Constrained Optimization 481
20 Multi-Objective Optimization 517
21 Expensive, Noisy and Dynamic Fitness Functions 563
A Some Practical Advice 607
B The No Free Luch Therorem and Performance Testing 613
C Benchmark Optimization Functions 641