FOUNDATIONS OF GENETIC PROGRAMMING

Published by Springer. ISBN 3-540-42451-2
Amazon USA UK.

More information, table of contents and code.

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Cute movie of a GP population evolving

Not so cute picture of bill presenting the book at GECCO'2001.
Complete tutorial slides for GECCO 2003 and (chapters 7 and 8) for CEC-2005 (PDF).
Picture of Ricardo Landa Becerra being awarded a prize copy at GECCO 2005 by Micheal O'Neill.

Table of Contents

Preface
Acknowledgements
Chapter 1.	Introduction	1
1.1	Problem Solving as Search	2
1.2	What is Genetic Programming?	9
1.3	Outline of the Book	15
Chapter 2.	Fitness Landscapes	17
2.1	Exhaustive Search	17
2.2	Hill Climbing	17
2.3	Fitness Landscapes as Models of Problem Difficulty	19
2.4	An Example GP Fitness Landscape	20
2.5	Other Search Strategies	21
2.6	Difficulties with the Fitness Landscape Metaphor	23
2.7	Effect of Representation Changes	25
2.8	Summary	26
Chapter 3.	Program Component Schema Theories	27
3.1	Price's Selection and Covariance Theorem	28
3.2	Genetic Algorithm Schemata	33
3.3	From GA Schemata\ to GP Schemata	35
3.4	Koza's Genetic Programming Schemata	38
3.5	Altenberg's GP Schema Theory	39
3.6	O'Reilly's Genetic Programming Schemata	42
3.7	Whigham's Genetic Programming Schemata	44
3.8	Summary	46
Chapter 4.	Pessimistic GP Schema Theories	47
4.1	Rosca's Rooted Tree Schemata	47
4.2	Fixed-Size-and-Shape Schemata\ in GP	49
4.3	Point Mutation and One-Point Crossover in GP	54
4.4	Disruption-Survival GP Schema Theorem	58
4.5	Summary	66
Chapter 5.	Exact GP Schema Theorems	67
5.1	Criticisms of Schema Theorems	67
5.2	The Role of Schema Creation	69
5.3	Stephens and Waelbroeck's GA Schema Theory	71
5.4	GP Hyperschema Theory	72
5.5	Examples	81
5.6	Exact Schema Theorem for GP with Standard Crossover	87
5.7	Summary	93
Chapter 6.	Lessons from the GP Schema Theory	95
6.1	Effective Fitness	95
6.2	Operator Biases and Linkage Disequilibrium for Shapes	103
6.3	Building Blocks in GAs and GP	105
6.4	Practical Ideas Inspired by Schema Theories	107
6.5	Convergence, Population Sizing, GP Hardness and Deception	108
6.6	Summary	109
Chapter 7.	The Genetic Programming Search Space	111
7.1	Experimental Exploration of GP Search Spaces	111
7.2	Boolean Program Spaces	112
7.3	Symbolic Regression	121
7.4	Side Effects, Iteration, Mixed Arity: Artificial Ant	122
7.5	Less Formal Extensions	125
7.6	Tree Depth	127
7.7	Discussion	128
7.8	Summary	130
Chapter 8.	The GP Search Space: Theoretical Analysis	131
8.1	Long Random Linear Programs	131
8.2	Big Random Tree Programs	137
8.3	XOR Program Spaces	143
8.4	Summary	148
Chapter 9.	Example I: The Artificial Ant	149
9.1	The Artificial Ant Problem	149
9.2	Size of Program and Solution Space	152
9.3	Solution of the Ant Problem	155
9.4	Fitness Landscape	156
9.5	Fixed Schema Analysis	157
9.6	The Solutions	165
9.7	Discussion	166
9.8	Reducing Deception	168
9.9	Summary	169
Chapter 10.	Example II: The Max Problem	173
10.1	The MAX Problem	174
10.2	GP Parameters	174
10.3	Results	174
10.4	Variety	181
10.5	Selection Pressure	184
10.6	Applying Price's Covariance and Selection Theorem	187
10.7	Summary	190
Chapter 11.	GP Convergence and Bloat	191
11.1	Convergence	191
11.2	Bloat	195
11.3	Subquadratic Bloat	200
11.4	Depth and Size Limits	209
11.5	Discussion	210
11.6	AntiBloat Techniques	212
11.7	Summary	214
Chapter 12.	Conclusions	217
Appendix	Genetic Programming Resources	221
Bibliography		223
List of Special Symbols		239
Glossary		245
Index		253

code Park & Miller pseudo random numbers, Santa Fe Ant trail (code and soloutions), ntrees.cc ntreeshapes.cc