- Adaptive Representations with Learning (ARL) 12
- ADF 11,12
- bloat 216
- search space 127
- alias problem 174
- Altenberg's GP schema theory 38-43
- ant problem 151-174
- search space 124-126
- symmetries 167
- arity 9
- ARL 12
- Automatically Defined Functions see ADF
- basin of attraction 18,19
- bias 131-132,217
- initialisation 109,170
- one-point crossover
- binary tree 106-107
- size 105,106
- ramped half-and-half 157
- removal 200
- required 201
- size 170,215
- standard crossover
- local search 106
- removal 200
- size 105
- uniform crossover
- global 106
- bloat
- ADF 216
- code editing 216
- depth limit 214
- effective fitness explanation 100-101
- genetic operators 215
- linear GP 204
- none one-point crossover 193
- Pareto 215
- parsimony pressure 215
- reduction techniques 214-216
- schema analysis example 103-105
- size limit 214
- tentative schema analysis 101-103
- Boolean functions, number of 114,115
- Boolean search spaces 113-123
- breed true 197
- building blocks 36,107-109
- Goldberg 36
- Max problem 182
- missing from ant 163-165,169
- O'Reilly 45
- one-point crossover 70
- cartesian node reference systems 90
- CFG-GP 45
- code
-
gp-simple.c 11
-
ntrees.cc 113
-
rand_tree.cc 113
- solutions to ant problem 154
- code editing for bloat prevention 216
- competing conventions 170
- component
- analysis 6
- instantiations 36
- position less 38
- positioned 38
- schema 35
- context free grammar GP see CFG-GP
- convergence
- bloat 193-217
- GA vs. GP 193
- linear search space, proof 133,139
- Markov analysis 27
- Max problem 175,179-190,192
- one-point 58-59,67
- phenotype 198-199,211
- population 110
- schema analysis 73-83
- tree search space
- ADF 127
- experimental 114-126
- loops 128-129
- memory 127-128
- proof 133,139-144
- shape 129-130
- XOR 146-150
- crossover 1-3,6,9-10
- D'haeseleer 60
- effect on reproduction rate 31
- GA vs. GP 194-197
- Koza see crossover, standard
- one-point 55-60
- effect on schemata 61
- implementation 58
- motivation 109
- no bloat 193
- Price's theorem 33
- PADO 15
- PDGP 14
- Price's theorem 31-32,201
- respectful, Radcliffe 59
- size fair 200
- smooth, motivation 109
- standard 10,11
- big trees 211
- bloat 202-211
- depth increase 216
- depth limit 211-213
- directed acyclic graph 217
- exact schema theorem 92-93
- protection from 200
- removal bias 200
- size change 199
- size limit 211-213
- strong context preserving 60
- subtree swapping see crossover, standard
- to reduce bloat 215
- two-point 60
- uniform 60,106,109
- data mining 213
- deception 110
- ant problem 169
- caused by adjusted fitness 98
- Max problem 175
- defining length
- fixed-size-and-shape schema 53
- O'Reilly's definition 43
- depth
- binary trees 129-130
- linear growth 205
- DGPC, maximum tree size 214
- Discipulus 132
- maximum program length 214
- disruption probability 34,44
- don't care symbol
- in binary GAs 33,52
- left and right 73
- lower and upper schema 75,76
- O'Reilly 43,55
- one node 52,53,55,77
- polymorphic 53
- Rosca 49-51,55
- subtree 52,77
- variable arity schema 91,94
- dynamical system models 3-5
- edit distance 194
- effective fitness 97-105
- bit string GAs 97-98
- exact 99-100
- early GP 98-99
- exact GP 100
- high 100
- standard crossover 100
- effort
- ant problem 174
- random search 157
- elitism
- tournament selection 187
- entropy
- during bloat 216
- population 194
- evolution of program depths 203-204
- evolution of program shapes 203-204
- evolution vs.\ optimisation 199
- evolvability 121
- exact schema theorem
- for standard crossover 42
- macroscopic 82
- exhaustive search 17
- ant problem 154
- false peaks 19
- ant problem 169,171
- feature selection 213
- fitness 9
- deceptive see deception
- evaluations required see effort
- offspring fitness correlation 32
- primitive covariance see Price's theorem
- program size see parsimony
- proportionate selection 34,60
- test set 9
- fitness landscapes 4-6,17-26
- ant problem 169
- dynamic 103
- effective 103-105
- XOR parity problems 148-150
- fixed-size-and-shape schema 51,53
- fluff see introns
- fractal trees 130-131
- fragility
- bit string GAs 34
- of node composition 63
- of shape 62,66,67
- Rosca 51
- generalisation 131-132
- generational model 9
- genetic drift 32,149
- Price's theorem 30
- geometric interpretation of GP schemata 55
- geometric interpretation of GP hyperplanes 55
- GLib 11
- global vs.\ local search tradeoff 21
- GP as evolutionary process 199
- GP easy 110
- GP point mutation 55-59
- GP schema 51
- defining length 53
- definition 53
- geometric interpretation 55
- length 53
- order 53
- Rosca's definition 50
- rooted tree 49-51
- Rosca's 49-51
- Whigham's definition 45
- GP schema theorem
- exact
- macroscopic 82
- microscopic 77
- schema creation correction 80
- survival-disruption version 65
- GP schema theory
- Altenberg's 38-43
- O'Reilly's 43-45
- pessimistic models 49
- Rosca's 49-51
- Whigham's 45-46
- GPQUICK, maximum tree size 214
- greedy search 5
- grey code 25
- hill climbing 17
- ant problem 152,158-160,171
- with restarts 21
- Holland's schema theorem 33
- generalisation 68
- hyperplanes 55
- hyperschema 77
- variable arity
- hyperspace 55
- implicit parallelism
- hypothesis 54
- Max problem 182
- ineffective code 199
- information in test set 139
- initialisation 109
- Max problem 176
- variety 183-184
- one-point crossover 59,66
- full 67
- ramped half-and-half 198,204,211,214
- ramped uniform 214
- ridge divide 213
- uniform 214
- instantiation 36-37
- happy faces 35-37
- Koza's 38
- O'Reilly's 43-45
- Rosca 50
- Whighams's 45
- introns 199
- ant problem 167
- ant solution 166,168
- effective fitness 98,101
- none in Max problem 176
- inviable code 199
- junk code 199
- karst landscape 169
- Koza's schema 38
- Levenshtein distance 194
- linear genetic programming 13,132-139
- local optima 19
- long-path problem 19
- loops, effect on search space 128-129
- machine code GP see linear genetic programming
- macroscopic exact GP schema theorem 82
- Markov analysis
- big trees 141
- GA 3-4,69
- too detailed 27
- long programs 135
- Max problem 175-192
- memory
- search space 127-128
- usage by GP 216-217
- directed acyclic graph 217
- microscopic exact GP schema theorem 77
- Monte Carlo see random sampling
- multi-cardinality GA 53
- multi-objective fitness
- ant problem 154
- bloat reduction 215
- problems with landscape metaphor 23
- multiple tree programs 12
- mutation 1-2
- bloat 199
- convergence, small trees 194
- GA effective fitness 97-98
- in GAs 33
- included in schema transmission 71
- non-convergence 193-197
- point 9,55-59,65
- ant problem neighbour operator 158
- convergence 67
- schema theorem 65
- Price's theorem 32
- size fair 217
- to reduce bloat 215
- Whigham's 45
- natural selection 1
- neutral network 174,201
- ant problem 152,169
- no free lunch 8-9
- ant problem 168-169
- number of Boolean functions 114
- O'Reilly
- don't care symbol 55
- GP schema theory 43-45
- Occam's Razor 215
- Ockham see Occam
- one-point crossover
- effect on schemata 61
- in GAs 33
- order
- ant problem 162-165,169
- fixed-size-and-shape schema 53
- GP building blocks 70
- GP schema 53
- in binary GAs 33
- O'Reilly's definition 43
- Rosca's definition 50
- PADO 14-15
- Parallel Distributed Genetic Programming see PDGP
- Pareto fitness bloat reduction 215
- parity
- number of solutions 147
- number of solutions using XOR 147
- search space
- 3 bit 118-119
- 4 bit 117-118
- 6 bit 119-123
- with XOR 144-150
- parsimony pressure 215
- PDGP 14-15
- ant problem 158
- pessimistic GP schema theories 49
- point mutation in GP 55-59
- Poli and Langdon's GP schemata 51
- population sizing 110
- Price's theorem 28
- genetic algorithms 31
- Max problem 187-190
- one-point crossover 33
- proof 29
- tournament selection 31
- program component schema theories 27
- program depths 203-204
- program shapes 203-204
- program size limit and Price's theorem 32-33
- programs, number of 115
- ramped half-and-half 113,130,198,204,211
- ant problem 152,157-158
- Max problem 176
- low variety 183
- one-point crossover 59,66
- random
- programs 132-133
- initialisation bias 109
- sextic polynomial 198
- sampling 113,131,133
- ant problem 152,154,157-159
- big programs 131
- convergence proof 133-144
- effort 157
- GP biases 213
- limit to bloat 211
- no free lunch 9,168-169
- trees 113-126,154
- XOR 144-150
- solution
- linear programs 139
- tree 144
- trees 129-131,203-204,214
- walk 201,203,216
- recombination see crossover
- recursion, search space 128-129
- register GP see linear genetic programming
- removal bias 200
- size fair crossover 200
- respect, crossover, Radcliffe 59
- rooted tree schemata 49-51
- Rosca
- Adaptive Representations with Learning 12
- bloat 197,203,215
- don't care symbol 55
- GP schema theory 49-51
- parity problem 119
- population entropy 194
- Santa Fe trail see ant problem
- schema 7
- Altenberg's 38-43
- as subexpressions 42
- bit string 33-35
- components
- position less 38
- positioned 38
- components, in binary GAs 35
- creation 71-73
- creation correction 80
- disruption 35
- disruption probability 34,44
- extinction 72
- fitness, in Altenberg's theorem 42
- fixed-size-and-shape 51
- fragility
- bit string GAs 34
- of node composition 63
- of shape 62,66,67
- Rosca 51
- in CFG-GP 45
- instantiations 36,37
- Koza's 38
- left and right parts 73-74
- O'Reilly's 43-45
- order
- ant problem 162-165,169
- fixed-size-and-shape 53
- GP building blocks 70
- in binary GAs 33
- O'Reilly's definition 43
- Rosca's definition 50
- Poli and Langdon's 51
- Rosca's 49-51
- signal-to-noise ratio 71-73
- transmission 71
- Whigham's 45-46
- schema theorem
- Altenberg's 38-43
- criticism 32,69-71
- Holland's 33
- generalisation 68
- introduction 7,8
- macroscopic, exact 82
- microscopic, exact 42,77
- O'Reilly's 43-45
- pessimistic models 49
- programs of fixed size and shape 74
- Rosca's 49-51
- schema creation correction 80
- standard crossover 92-93
- microscopic 42
- Stephens and Waelbroeck's 73
- survival-disruption version 65
- Whigham's 45-46
- schemata see schema
- search and problem solving 2
- search bias see bias
- search space 2
- 3 bit parity 118-119
- 4 bit parity 117-118
- 6 bit parity 119-123
- ADF 127
- ant problem 124-126
- Boolean 113-123
- linear programs
- convergence proof 133-139
- random solution 139
- loops 128-129
- memory 127-128
- parity, with XOR 144-150
- recursion 128-129
- symbolic regression 123-124
- tree, convergence proof 139-144
- XOR in random NAND gates 116-117
- selection see fitness proportionate or tournament
- sex see crossover
- sextic polynomial
- bloat 199
- fitness covariance 202
- phenotypic convergence 200
- random programs 198
- search space 123-124
- tree shapes 204
- shape
- evolution of 205
- simulated annealing 21
- ant problem 152,158
- bloat 199
- smooth crossover see crossover, smooth
- stack based GP 14
- steady state model 9
- Stephens and Waelbroeck's GA schema theory 73
- symbolic regression see sextic polynomial
- syntax trees 9
- tabu search 21
- test set, information content 139
- tournament selection pressure
- elite fraction 187
- Max problem 186-190
- tree
- depth and size 129-130
- depths 203-204
- distribution of 204
- fractal 130-131
- number of 204
- shape 130-131,203-204
- tree fragment see schema
- uniform crossover see crossover, uniform
- variable arity hyperschema 90-92
- Whigham's GP schema theory 45-46,55
- wild card symbol see don't care symbol
- XOR problem, search space 116-117
ucacbbl
2002-10-19