Biologically Inspired Optimization Methods: An Introduction

✍ Scribed by Mattias Wahde

• Publisher: WIT Press
• Year: 2008
• Tongue: English
• Leaves: 241
• Edition: 1
• Category: Library

✦ Synopsis

Biologically inspired optimization methods constitute a rapidly expanding field of research, with new applications appearing on an almost daily basis, as optimization problems of ever-increasing complexity appear in science and technology. This book provides a general introduction to such optimization methods, along with descriptions of the biological systems upon which the methods are based. The book also covers classical optimization methods, making it possible for the reader to determine whether a classical optimization method or a biologically inspired one is most suitable for a given problem. The book is primarily intended as a course book for students with a background covering basic engineering mathematics and elementary computer programming. Each method is illustrated with several basic examples, as well as more complex examples taken from the research literature. In addition, several exercises are provided, ranging from basic theoretical questions to programming examples. While theoretical results are presented, the book is mainly centered on practical applications of the optimization methods considered.

✦ Table of Contents

Cover......Page 1
Biologically Inspired Optimization Methods......Page 4
Copyright Page......Page 5
Contents......Page 8
Abbreviations......Page 12
Preface......Page 14
Notation......Page 18
Acknowledgements......Page 20
1.1 The importance of optimization......Page 22
1.2 Inspiration from biological phenomena......Page 23
1.3 Optimization of a simple behaviour for an autonomous robot......Page 26
2.1 Introduction......Page 30
2.2 Taxonomy of optimization problems......Page 32
2.3 Continuous optimization......Page 33
2.4 Algorithms for continuous optimization......Page 37
2.5 Limitations of classical optimization......Page 54
Exercises......Page 55
3.1 Biological background......Page 56
3.2 Genetic algorithms......Page 61
3.3 Linear genetic programming......Page 93
3.4 Interactive evolutionary computation......Page 99
3.5 Biological vs. artificial evolution......Page 103
3.6 Applications......Page 104
Exercises......Page 117
4. Ant colony optimization......Page 120
4.1 Biological background......Page 121
4.2 Ant algorithms......Page 125
4.3 Applications......Page 132
Exercises......Page 137
5.1 Biological background......Page 138
5.2 Algorithm......Page 141
5.3 Properties of PSO......Page 145
5.4 Discrete versions......Page 150
5.5 Applications......Page 151
Exercises......Page 158
6. Performance comparison......Page 160
6.1 Unconstrained function optimization......Page 161
6.2 Constrained function optimization......Page 164
6.3 Optimization of feedforward neural networks......Page 166
6.4 The travelling salesman problem......Page 167
A.1 Biological background......Page 172
A.2 Artificial neural networks......Page 177
A.3 Applications......Page 193
B.1 Classical optimization......Page 194
B.2 Genetic algorithms......Page 195
B.3 Ant colony optimization......Page 204
B.4 Particle swarm optimization......Page 209
C.1 Hypothesis evaluation......Page 214
C.2 Experiment design......Page 221
D. Benchmark functions......Page 226
D.2 The Rosenbrock function......Page 227
D.3 The Sine square function......Page 228
D.5 A multidimensional benchmark function......Page 229
Answers to selected exercises......Page 230
Bibliography......Page 232
D......Page 236
M......Page 237
S......Page 238
W......Page 239

✦ Subjects

Биологические дисциплины;Матметоды и моделирование в биологии;