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A Field Guide to Genetic Programming |
Author: Riccardo Poli
Published: 2008-03-26 |
List price: $15.50
Our price: $13.95
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Customer comments on this selection.
 Very good start, simple, clear and free
[...]
You can see the Authors website and download a free pdf version of this site. Its available via creative commons non-commercial.
I studied this book and it gave me a great understanding of the material(from programming understanding). Very clear, concise and to the point. It gives great examples and gets you up to speed.
I wish more books about software in general were written in this form of a field guide.
These guys deserve a medal.
 The best introductory book about Genetic Programming
"A Field Guide to Genetic Programming" is the best introductory book about the growing research area of Genetic Programming (GP). Written by some of the leading researchers in the field, the book explains very well the basic concepts of GP and gives a condensed state of the art of the technology. For practitioners, the book offers free Java-based software, called TinyGP and examples of many successful applications. A big benefit of the book is the comprehensive bibliography.
With its popular style and low price, "A Field Guide to Genetic Programming" can open the field to a very broad audience and create a breakthrough in GP applications.
Destined to become the standard reference to the field
This book is a comprehensive introduction to GP, and overview of the state of the art of the field, written by the arguably most important reserachers in the field. In other words, everything you could ask for. As a practicing evolutionary computation reserarcher, it gave me a number of new insights about the particular issues involved in evolving programs represented (mostly) as expression trees, and also a good sense of where the current big issues in the field are.
Table of Contents
1 Introduction 1
1.1 Genetic Programming in a Nutshell . . . . . . . . . . . . . . . 2
1.2 Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Overview of this Field Guide . . . . . . . . . . . . . . . . . . 4
Part I Basics 7
2 Representation, Initialisation and Operators in Tree-based GP 9
2.1 Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Initialising the Population . . . . . . . . . . . . . . . . . . . . 11
2.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Recombination and Mutation . . . . . . . . . . . . . . . . . . 15
3 Getting Ready to Run Genetic Programming 19
3.1 Step 1: Terminal Set . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Step 2: Function Set . . . . . . . . . . . . . . . . . . . . . . . 20
3.2.1 Closure . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.2 Sufficiency . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.3 Evolving Structures other than Programs . . . . . . . 23
3.3 Step 3: Fitness Function . . . . . . . . . . . . . . . . . . . . . 24
3.4 Step 4: GP Parameters . . . . . . . . . . . . . . . . . . . . . 26
3.5 Step 5: Termination and solution designation . . . . . . . . . 27
4 Example Genetic Programming Run 29
4.1 Preparatory Steps . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Step-by-Step Sample Run . . . . . . . . . . . . . . . . . . . . 31
4.2.1 Initialisation . . . . . . . . . . . . . . . . . . . . . . . 31
4.2.2 Fitness Evaluation . . . . . . . . . . . . . . . . . . . . 32
4.2.3 Selection, Crossover and Mutation . . . . . . . . . . . 32
4.2.4 Termination and Solution Designation . . . . . . . . . 35
Part II Advanced Genetic Programming 37
5 Alternative Initialisations and Operators in Tree-based GP 39
5.1 Constructing the Initial Population . . . . . . . . . . . . . . . 39
5.1.1 Uniform Initialisation . . . . . . . . . . . . . . . . . . 40
5.1.2 Initialisation may Affect Bloat . . . . . . . . . . . . . 40
5.1.3 Seeding . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.2 GP Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.2.1 Is Mutation Necessary? . . . . . . . . . . . . . . . . . 42
5.2.2 Mutation Cookbook . . . . . . . . . . . . . . . . . . . 42
5.3 GP Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.4 Other Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 46
6 Modular, Grammatical and Developmental Tree-based GP 47
6.1 Evolving Modular and Hierarchical Structures . . . . . . . . . 47
6.1.1 Automatically Defined Functions . . . . . . . . . . . . 48
6.1.2 Program Architecture and Architecture-Altering . . . 50
6.2 Constraining Structures . . . . . . . . . . . . . . . . . . . . . 51
6.2.1 Enforcing Particular Structures . . . . . . . . . . . . . 52
6.2.2 Strongly Typed GP . . . . . . . . . . . . . . . . . . . 52
6.2.3 Grammar-based Constraints . . . . . . . . . . . . . . . 53
6.2.4 Constraints and Bias . . . . . . . . . . . . . . . . . . . 55
6.3 Developmental Genetic Programming . . . . . . . . . . . . . 57
6.4 Strongly Typed Autoconstructive GP with PushGP . . . . . 59
7 Linear and Graph Genetic Programming 61
7.1 Linear Genetic Programming . . . . . . . . . . . . . . . . . . 61
7.1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . 61
7.1.2 Linear GP Representations . . . . . . . . . . . . . . . 62
7.1.3 Linear GP Operators . . . . . . . . . . . . . . . . . . . 64
7.2 Graph-Based Genetic Programming . . . . . . . . . . . . . . 65
7.2.1 Parallel Distributed GP (PDGP) . . . . . . . . . . . . 65
7.2.2 PADO . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
7.2.3 Cartesian GP . . . . . . . . . . . . . . . . . . . . . . . 67
7.2.4 Evolving Parallel Programs using Indirect Encodings . 68
8 Probabilistic Genetic Programming 69
8.1 Estimation of Distribution Algorithms . . . . . . . . . . . . . 69
8.2 Pure EDA GP . . . . . . . . . . . . . . . . . . . . . . . . . . 71
8.3 Mixing Grammars and Probabilities . . . . . . . . . . . . . . 74
9 Multi-objective Genetic Programming 75
9.1 Combining Multiple Objectives into a Scalar Fitness Function 75
9.2 Keeping the Objectives Separate . . . . . . . . . . . . . . . . 76
9.2.1 Multi-objective Bloat and Complexity Control . . . . 77
9.2.2 Other Objectives . . . . . . . . . . . . . . . . . . . . . 78
9.2.3 Non-Pareto Criteria . . . . . . . . . . . . . . . . . . . 80
9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80
9.4 Multi-objective Optimisation via Operator Bias . . . . . . . . 81
10 Fast and Distributed Genetic Programming 83
10.1 Reducing Fitness Evaluations/Increasing their Effectiveness . 83
10.2 Reducing Cost of Fitness with Caches . . . . . . . . . . . . . 86
10.3 Parallel and Distributed GP are Not Equivalent . . . . . . . . 88
10.4 Running GP on Parallel Hardware . . . . . . . . . . . . . . . 89
10.4.1 Masterslave GP . . . . . . . . . . . . . . . . . . . . . 89
10.4.2 GP Running on GPUs . . . . . . . . . . . . . . . . . . 90
10.4.3 GP on FPGAs . . . . . . . . . . . . . . . . . . . . . . 92
10.4.4 Sub-machine-code GP . . . . . . . . . . . . . . . . . . 93
10.5 Geographically Distributed GP . . . . . . . . . . . . . . . . . 93
11 GP Theory and its Applications 97
11.1 Mathematical Models . . . . . . . . . . . . . . . . . . . . . . 98
11.2 Search Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
11.3 Bloat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
11.3.1 Bloat in Theory . . . . . . . . . . . . . . . . . . . . . 101
11.3.2 Bloat Control in Practice . . . . . . . . . . . . . . . . 104
Part III Practical Genetic Programming 109
12 Applications 111
12.1 Where GP has Done Well . . . . . . . . . . . . . . . . . . . . 111
12.2 Curve Fitting, Data Modelling and Symbolic Regression . . . 113
12.3 Human Competitive Results the Humies . . . . . . . . . . . 117
12.4 Image and Signal Processing . . . . . . . . . . . . . . . . . . . 121
12.5 Financial Trading, Time Series, and Economic Modelling . . 123
12.6 Industrial Process Control . . . . . . . . . . . . . . . . . . . . 124
12.7 Medicine, Biology and Bioinformatics . . . . . . . . . . . . . 125
12.8 GP to Create Searchers and Solvers Hyper-heuristics . . . . 126
12.9 Entertainment and Computer Games . . . . . . . . . . . . . . 127
12.10The Arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
12.11Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
13 Troubleshooting GP 131
13.1 Is there a Bug in the Code? . . . . . . . . . . . . . . . . . . . 131
13.2 Can you Trust your Results? . . . . . . . . . . . . . . . . . . 132
13.3 There are No Silver Bullets . . . . . . . . . . . . . . . . . . . 132
13.4 Small Changes can have Big Effects . . . . . . . . . . . . . . 133
13.5 Big Changes can have No Effect . . . . . . . . . . . . . . . . 133
13.6 Study your Populations . . . . . . . . . . . . . . . . . . . . . 134
13.7 Encourage Diversity . . . . . . . . . . . . . . . . . . . . . . . 136
13.8 Embrace Approximation . . . . . . . . . . . . . . . . . . . . . 137
13.9 Control Bloat . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
13.10Checkpoint Results . . . . . . . . . . . . . . . . . . . . . . . . 139
13.11Report Well . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
13.12Convince your Customers . . . . . . . . . . . . . . . . . . . . 140
14 Conclusions 141
Part IV Tricks of the Trade 143
A Resources 145
A.1 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
A.2 Key Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
A.3 Key International Meetings . . . . . . . . . . . . . . . . . . . 147
A.4 GP Implementations . . . . . . . . . . . . . . . . . . . . . . . 147
A.5 On-Line Resources . . . . . . . . . . . . . . . . . . . . . . . . 148
B TinyGP 151
B.1 Overview of TinyGP . . . . . . . . . . . . . . . . . . . . . . . 151
B.2 Input Data Files for TinyGP . . . . . . . . . . . . . . . . . . 153
B.3 Source Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
B.4 Compiling and Running TinyGP . . . . . . . . . . . . . . . . 162
Bibliography 167
Index 225
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