Page 242 - 49A Field Guide to Genetic Programming

228 INDEX

evolutionary quantum computing, 120 FPGA, 92
evolutionary search, mathematical models, GP implementation, 93
98 fractal compression, 129
evolving agents, 122 freeware, 148
evolving designs, 23 ECJ, 148
example GPC++, 148
crossover, 34 Lil-GP, 148
ephemeral random constant, 29 Open Beagle, 148
fitness, 30 TinyGP, 151–162
function set, 30 frequency of primitives, 136
mutation, 33 full random trees, 12–13
parameter, 30 function arity, 11
terminal set, 29 function set, 19–23
termination, 31 evolving non-programs, 23
exceptions example, 30
integer overflow, 22 modelling, 115
problems caused by trapping, 22 side effects, 24
exchange rate, 123 sufficiency, 22–23
executable model, bloat, 102 function-defining branch, 48
expression simplification, 135
extended compact genetic algorithm gambling, 123
(eCGA), 70 game theory, 123
extended compact GP (eCGP), 72 games, 127
generalisation-accuracy tradeoff, 107
farming in parallel GP, 89 generalised ant programming (GAP), 74
feature selection, 115 genetic operator, 2
field programmable gate array (FPGA), 92 rates, 26, 116
films, feature, 128 genetic program representation, 9
films, GP, 146 GENR8, 76
financial time series prediction, 123–124 geographically distributed GP, 93–95
fine-grained distributed GP, 94 GP implementations, 147–148
finite state automata, 66 TinyGP, 151–162
fitness, 2, 24–26 GP problem solver (GPPS), 51
anytime, 85 GP-ZIP, 130
case, 26 GPC++, implmentation, 148
reduction, 83 GPU, 90
combined objectives, 75–76 speedup factor, 92
dynamic, 84 grammar, 53
dynamic, multi-objective, 80–81 based constraint, 53
example, 30 based GP, 53
fast initialisation, 57
FPGA, 92 operators, 57
GPU, 90–92 context-sensitive, 55
sub-machine-code, 93 tree adjoining, 55
hits, 76 grammar model based program evolution
incremental, 58 (GMPE), 74
multi-objective, 75–80 grammatical evolution
image processing, 122 troubleshooting, 134
RMS, 115 grammatical evolution, 55
sharing, 77, 137 graph, directed acyclic (DAG), 87
staged, 81, 85 graph-based GP, 65–68
static, problems with, 84 graphics processing unit (GPU), 90
symbolic regression, 115 grow random trees, 12–14
for, syntax constraint, 52 tree size bias, 13
Fourier transform, quantum evolution, 120 growth vs. bloat, 101