Customer Reviews

A Field Guide to Genetic Programming

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"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.

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.

The Field Guide is a clear and concise introduction and reference to all things genetic programming related. It has an excellent introduction to the subject generally, and a good summary of several related theoretical matters. The best part is the review of practical applications, which is very broad. This review incorporates the authors' opinions on when and where GP is usefully applied - given the detailed survey of applications that follows, I trust them.

Good quick intro. Clearly written.
Code samples are in Java.
[...]

This book is a comprehensive introduction to GP and reference to all genetic programming related issues.
It also provides a good number of related subjects and a good summary of several related theoretical matters.
Includes source code file in Java.

This book was immediately added to the bookshelf where my favourites, and most useful, are stored.

It explains everything you need to know to write a GP system. If you need it to be even more explicit, then it even includes the code for a complete GP system, called TinyGP, at the back of the book.

In addition, it describes a variety of related content, such as using parallel systems to implement GP and probabilistic GP. It also provides a good number of references to interesting related subjects.

Short and concise, and yet wide ranging in subject matter. What else do you need? It's the only book I have on GP and, not being in that field, the only GP book I will need for the forseeable future.

It is well written, and very easy to read, making it a fun read even if you're just curious about GP.

The book is simple short, but in the same time has enough depth and good explanations. It has lots of links to find more information (bibliography ~40-50 pages which at first looked too much for me). And its price can't get any better :) Good value for the money and enjoyable read.

The book is a great introductory book as it gives many examples how GP has performed in various fields. What I wanted most was to be able to recognize if a problem is a good candidate for GP or genetic algorithms in general. This book helped me do that. The book can be found for free online or you can purchase this copy, unplug , and learn a lot in only a few hours. Seriously though, at this price you might as well buy it because if you are at all interested in the field you will be glad you did. You can also use it to show your friends what GP is all about. I for one didn't want to keep sounding too far 'out there' to intelligent people who were distant from the field and needed some easy reading material to understand what I was talking about.

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