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A field guide to genetic programming

Poli, R; Langdon, WB; McPhee, NF; (2008) A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk

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3 Getting Ready to Run Genetic Programming 3.1 Step 1: Terminal Set 3.2 Step 2: Function Set 3.2.1 Closure 3.2.2 Sufficiency 3.2.3 Evolving Structures other than Programs 3.3 Step 3: Fitness Function 3.4 Step 4: GP Parameters 3.5 Step 5: Termination and solution designation 4 Example Genetic Programming Run 29 4.1 Preparatory Steps 4.2 Step-by-Step Sample Run 4.2.1 Initialisation 4.2.2 Fitness Evaluation 4.2.3 Selection, Crossover and Mutation 4.2.4 Termination and Solution Designation Part II Advanced Genetic Programming 5 Alternative Initialisations and Operators in Tree-based GP 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 6 Modular, Grammatical and Developmental Tree-based GP 6.1 Evolving Modular and Hierarchical Structures 6.1.1 Automatically Defined Functions 6.1.2 Program Architecture and Architecture-Altering 6.2 Constraining Structures 6.2.1 Enforcing Particular Structures 6.2.2 Strongly Typed GP 6.2.3 Grammar-based Constraints 6.2.4 Constraints and Bias 6.3 Developmental Genetic Programming 6.4 Strongly Typed Autoconstructive GP with PushGP 7 Linear and Graph Genetic Programming 7.1 Linear Genetic Programming 7.1.1 Motivations 7.1.2 Linear GP Representations 7.1.3 Linear GP Operators 7.2 Graph-Based Genetic Programming 7.2.1 Parallel Distributed GP (PDGP) 7.2.2 PADO 7.2.3 Cartesian GP 7.2.4 Evolving Parallel Programs using Indirect Encodings 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 8.2 Pure EDA GP 8.3 Mixing Grammars and Probabilities 9 Multi-objective Genetic Programming 9.1 Combining Multiple Objectives into a Scalar Fitness Function 9.2 Keeping the Objectives Separate 9.2.1 Multi-objective Bloat and Complexity Control 9.2.2 Other Objectives 9.2.3 Non-Pareto Criteria 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 9.4 Multi-objective Optimisation via Operator Bias 10 Fast and Distributed Genetic Programming 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 10.2 Reducing Cost of Fitness with Caches 10.3 Parallel and Distributed GP are Not Equivalent 10.4 Running GP on Parallel Hardware 10.4.1 Master slave GP 10.4.2 GP Running on GPUs 10.4.3 GP on FPGAs 10.4.4 Sub-machine-code GP 10.5 Geographically Distributed GP 11 GP Theory and its Applications 11.1 Mathematical Models 11.2 Search Spaces 11.3 Bloat 11.3.1 Bloat in Theory 11.3.2 Bloat Control in Practice Part III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers Hyper-heuristics 12.9 Entertainment and Computer Games 12.10 The Arts 12.11 Compression 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Part IV Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources B TinyGP B.1 Overview of TinyGP B.2 Input Data Files for TinyGP B.3 Source Code B.4 Compiling and Running TinyGP Bibliography Index

Type: Book
Title: A field guide to genetic programming
ISBN-13: 978-1-4092-0073-4
Publisher version: http://www.gp-field-guide.org.uk/
Additional information: (With contributions by J. R. Koza) keywords: genetic algorithms, genetic programming, cartesian genetic programming, automatic programming, machine learning, artificial intelligence, evolutionary computation notes: http://www.gp-field-guide.org.uk/ size: 250 pages
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1327753
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