- title = "Genetic Programming Theory and Practice XVIII",
- year = "2021",
- editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel",
- series = "Genetic and Evolutionary Computation",
- address = "East Lansing, USA",
- month = "19-21 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-981-16-8112-7",
-
URL = "
https://link.springer.com/book/9789811681127",
-
DOI = "
doi:10.1007/978-981-16-8113-4",
- size = "XVIII + 212",
-
abstract = "Note GPTP 2020 not held due to corvid
pandemic
Contents \cite{Bayer:2021:GPTP}, \cite{Dolson:2021:GPTP}, \cite{Fleck:2021:GPTP}, \cite{Fonseca:2021:GPTP}, \cite{Guadalupe-Hernandez:2021:GPTP}, \cite{Korns:2021:GPTP}, \cite{Kotanchek:2021:GPTP}, \cite{Langdon:2021:GPTP}, \cite{Miller:2021:GPTP}, \cite{Saini:2021:GPTP}, \cite{Sloss:2021:GPTP},
Index
A Action program, 2 multi-action program, 6 Activity dependence, 166 Ascension, 203 Automated program repair, 46
B Bees algorithm, 117 Benchmarking, 8, 84
C Cache, 52, 161 Cambrian explosion, 199 Causality, 71 Classification, 166 Co-evolution, 202 Competition, 89, 114, 205 Context-free grammar, 48 Convergence phenotypic, 150, 199 Crossover asymmetry of GP subtree crossover, 151 fatherless crossover, 158 unbiased subtree crossover, 150
D Data balancing, 133, 141 Deep learning, 2, 109, 165 with genetic programming, 109 Diagnostics exploration diagnostics, 104 selection scheme diagnostics, 104 Discriminant analysis, 113 Diversity, 2, 53, 63, 88, 139, 199 phenotypic, 64 phenotypic diversity, 89 phylogenetic, 64 phylogenetic diversity, 84
E Eco-EA, 66 Efficiency, 84, 114, 129, 155, 203 Ensembles, 133, 138, 139 Exploration diagnostic, 67 Exponential growth, 206
F Feedback loop, 71 Fitness predicting evaluation time of, 160 Fitness sharing, 66
G General artificial intelligence, 165 Genetic learning, 182 Genetic programming BalancedGP, 133, 137 grammar-based vectorial GP, 22 networked runs genetic programming, 109 OrdinalGP, 134, 137 PushGP, 52, 102, 190 template-constrained genetic program- ming, 45, 109 vectorial GP, 22 Grammar-guided, 22 Graph, 28, 111, 183 Growing neural networks, 111, 168
H High performance, 143, 195 Homeostatis, 172 Horizontal gene transfer, 203
I Inefficient threads avoiding, 143 causes, 143 measurement, 143 prediction, 143 Information loss, 33, 40 Inplace crossover, 143 shuffle, 143 speedup, 143 Intellectual property, 202
L Lexicase selection, 65, 66, 83, 191 cohort lexicase selection, 83 down-sampled lexicase selection, 83 epsilon lexicase selection, 83 novelty lexicase selection, 83 Linear genetic programming, 3, 7, 18, 69, 184 Liquid types, 50, 51
M Memory bandwidth, 143 Memory use minimising, 143 Metrics, 70 Mitochondria, 203 Modular, 167, 194 Modularity, 2, 7, 69, 181, 194 Moore Law, 197, 206
N Novelty, 90, 199
P Panmictic, 146, 150 Parent selection, 65, 83, 191 Pareto tournament, 131 Partially observable, 1 Population diversity, 2, 66, 97, 199 Population initialization, 2, 5, 12, 55, 90, 174 Predicting success based on diversity, 63 Program dendrite program, 168 evolving modular program, 182 neuron program, 176 program graph, 2, 183 program representation, 46 program synthesis, 47, 52, 84 program synthesis benchmark suite, 190 programming languages, 48 Program synthesis, 47, 84, 190 program synthesis benchmark suite, 190
R Rampant mutation, 2 Reinforcement learning, 2, 17, 176 Resilience, 203, 207
S Selection offspring selection, 34 selection pressure, 34, 157, 161 Semantic constraints, 48 Semiconductor industry, 197 SMT solvers, 48, 58 Social evolution, 203, 205 Strongly-typed, 25 Sustainability, 202 Symbolic regression, 24, 30, 87, 88, 115, 116
T Tags, 183 Tangled program graphs, 2, 183 Team, 3, 183 Tournament selection, 65, 85, 90, 91, 103, 130, 143, 145, 150, 161 Tree-based GP, 26, 28 Tree depth, 57 Type-aware, 50",
- notes = "Published after the workshop in 2022",
