Page 241 - 49A Field Guide to Genetic Programming

INDEX 227

conference submission, 139 toroidal grid, 94
conferences, 145, 147 density of solutions, 57
constant, ephemeral random, 20 depth
constraints node, 12
bias, 55 tree, 12
enforced by crossover etc, 53 depth limits, 104
grammar-based, 53 derivation tree, 54
on tree size or depth, 104 developmental GP, 23, 57
dynamic, 105 direct problem, 112
on tree structure, 52 directed acyclic graph (DAG), 87
reduction of search space, 55 DirectX, 90
semantic, 56 disassortative mating, 82
strong typing, 52 Discipulus, 63, 124
syntactic, 56 discussion group, 149
context-preserving crossover, 45 distributed
theory, 98 evolutionary algorithm, 88–95
context-sensitive grammar, 55 GP, 93–95
control populations
proportional integrative and deriva- fine-grained, 94
tive (PID), 119 geographically, 93–95
robot, 119 ring topology, 94
Core War, 120 toroidal grid, 94
covariant parsimony pressure, 107 distribution
crossover sampling, 69
between ADFs, 49 diversity, 94
bias theory, 104 promotion, multi-objective GP, 78
Cartesian GP, 68 Draughts, 127
constrained, 52 dynamic fitness, 84
context-preserving, 45, 98 dynamic size or depth limits, 105
depth based, 45 dynamic subset selection (DSS), 85
example, 34
FPGA, 93 EA, 1
headless chicken, 16 EC, 1
homologous, 44, 98 ECJ, Java implementation, 148
linear GP, homologous, 64 economic modelling, 123–124
one-point, 44 editing operator, 46
operator, 2 efficient market hypothesis, 123
PDGP, 66 elitism, troubleshooting, 137
point, 15 Elvis robot, 115
rate, 17 embarrassingly parallel, 89
size-fair, 45, 98, 105 engine monitoring and control, 121
subtree, 15 entropy, 129
uniform, 44 ephemeral random constant, 20
curve fitting, 113–116 example, 29
estimation of distribution algorithms
data cache, 86 (EDAs), 69
data flow GP, 65 estimation of distribution programming
data mining, 85 (EDP), 72
many variables, 125 evaluation safety, 22
medical, 125 even parity, 78
data modelling, 113–116 evolutionary algorithm (EA), 1
data visualisation, 135 distributed, 88–95
virtual reality, 80 evolutionary art, 128
deme, 93 evolutionary computation (EC), 1
ring topology, 94 evolutionary music, 128
100 1000 2
Avg Size Avg Fitness sin(x)
Best Fitness GP (gen=92)
90
1.5
80
1
70
100
0.5
60
Generation 92 Average Size 50 Fitness 0
40 -0.5
10
30
(see Sec. B.4) 20 -1
-1.5
10
1 -2
0 20 40 60 80 100 0 20 40 60 80 100 0 1 2 3 4 5 6
Generations Generations x