Page 244 - 49A Field Guide to Genetic Programming
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230                                                        INDEX


            machine intelligence, 1           N-gram GP, 72
                human-competitive, 141        NAND, distribution of circuits, 99
            machine learning (ML), 1          nature of program search spaces theory, 102
            mailing list, GP, iii, 149        neural network evolution, 121
            Markov chain model                    with PDGP, 66
                evolutionary algorithms, 98   niching, 77
                GP, 98                        NLP
                program execution, 100            parsing and tagging, multi-objective
            master–slave GP, 89                       GP, 79
            max problem, 72                       text retrieval, multi-objective GP, 79
            medical imaging, 122              node
            message passing interface (MPI), 95   arity, 11
            meta-heuristics, 126                  depth, 12
            meta-optimising  semantic  evolutionary  replacement mutation, 43
                    search (Moses), 72        non-terminal symbols, 53
            migration rate, 94                non-Turing complete program, theory, 99
                                              NSGA-II, extension to GP, 79
            MIMIC, 70
            minimum description length (MDL), 46,  nuclear reactor control, 124
                    107                       numeric regression, 30
                                              numerical control, 124
            model car racing, 127
            model, executable, 102
            modular structure, 47             Odin, 51
                                              one-max, 72
            modules, 59
            Moore’s Law, 90                   one-point crossover, 44
                                                  theory, 98
            MPI, 95
            multi-level type systems, 53      OpenGL, 92
                                              operator
            multi-objective fitness, 75–76
                image processing, 122             architecture-altering, 50
                                                  composition, 17
            multi-objective GP (MO GP), 41, 75    constrained, 53
                jets, 125
                                                  crossover, 2, 34
                operator pressure, 81             editing, 46
                Pareto dominance, 76–80           genetic, 2
                small vs good, 81
                                                  grammar-based GP, 57
            multi-objective optimisation (MOO), 75  mutation, 2
                data visualisation, 80
                                                  rate, 17
                preference information, 77        reorganisation of subtrees, 46
            multiple typed programs, 21–22
                                                  repair, 81
            music, evolutionary, 128              reproduction, 17, 33
            mutation, 2                       optical character recognition (OCR), 122
                constants, 43                 Oscar, 128
                dynamic libraries, 48         Othello, 127
                example, 33                   over-fitting, 46, 139, 140
                hoist, 43, 106                    dynamic fitness function, 84
                local search, 43              overflow, numeric, 22
                node replacement, 43
                permutation, 43               Pac-Man, Ms, 127
                point, 16, 43                 PADO
                rate, 17                          anytime programming, 67
                shrink, 43, 106                   random access memory, 67
                simulated annealing, 44       panmictic population, 137
                size-fair, 42, 105            Paragen, 68
                subtree, 16                   parallel computing, 88
                survey, 42–44                 parallel distributed GP (PDGP), 65
                swap, 43                          ADF, 66
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