Page 13 - 49A Field Guide to Genetic Programming

CONTENTS CONTENTS

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 Master–slave 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

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

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