Page 12 - 49A Field Guide to Genetic Programming
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CONTENTS                                              CONTENTS


                   4.2.2  Fitness Evaluation . . . . . . . . . . . . . . . . . . . .  32
                   4.2.3  Selection, Crossover and Mutation . . . . . . . . . . .  32
                   4.2.4  Termination and Solution Designation . . . . . . . . .  35



            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

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