Essentials of Metaheuristics

List of Algorithms......Page 1
What is a Metaheuristic?......Page 9
Algorithms......Page 10
Notation......Page 11
Gradient-based Optimization......Page 13
Hill-Climbing......Page 17
The Meaning of Tweak......Page 19
Single-State Global Optimization Algorithms......Page 20
Adjusting the Modification Procedure: 1 + 1, (1 + lambda), and (1, lambda)......Page 23
Simulated Annealing......Page 25
Tabu Search......Page 26
Iterated Local Search......Page 28
Population Methods......Page 31
Evolution Strategies......Page 33
Mutation and Evolutionary Programming......Page 35
The Genetic Algorithm......Page 36
Crossover and Mutation......Page 37
More Recombination......Page 41
Selection......Page 43
Elitism......Page 46
The Steady-State Genetic Algorithm......Page 47
The Tree-Style Genetic Programming Pipeline......Page 48
Hybrid Evolutionary and Hill-Climbing Algorithms......Page 49
Scatter Search......Page 51
Differential Evolution......Page 53
Variations on the Theme of Exploitation versus Exploration......Page 54
Particle Swarm Optimization......Page 55
Representation......Page 59
Vectors......Page 61
Initialization and Bias......Page 62
Mutation......Page 63
Recombination......Page 64
Direct Encoded Graphs......Page 65
Initialization......Page 68
Mutation......Page 69
Recombination......Page 70
Trees and Genetic Programming......Page 72
Initialization......Page 74
Recombination......Page 76
Mutation......Page 77
Forests and Automatically Defined Functions......Page 78
Strongly-Typed Genetic Programming......Page 79
Cellular Encoding......Page 80
Stack Languages......Page 81
Lists, Machine-Language Genetic Programming, and Grammatical Evolution......Page 82
Mutation......Page 84
Recombination......Page 85
Rulesets......Page 86
State-Action Rules......Page 87
Production Rules......Page 88
Initialization......Page 90
Bloat......Page 92
Parallel Methods......Page 95
Multiple Threads......Page 97
Island Models......Page 99
Master-Slave Fitness Assessment......Page 101
Spatially Embedded Models......Page 103
Coevolution......Page 105
1-Population Competitive Coevolution......Page 107
Relative Internal Fitness Assessment......Page 109
2-Population Competitive Coevolution......Page 113
N-Population Cooperative Coevolution......Page 118
Niching......Page 123
Fitness Sharing......Page 124
Crowding......Page 126
Multiobjective Optimization......Page 129
Naive Methods......Page 130
Non-Dominated Sorting......Page 133
Pareto Strength......Page 138
Combinatorial Optimization......Page 143
General-Purpose Optimization and Hard Constraints......Page 144
Greedy Randomized Adaptive Search Procedures......Page 147
Ant Colony Optimization......Page 148
The Ant System......Page 149
The Ant Colony System......Page 152
Guided Local Search......Page 154
Model Fitting by Classification......Page 157
Model Fitting with a Distribution......Page 160
Univariate Estimation of Distribution Algorithms......Page 163
Multivariate Estimation of Distribution Algorithms......Page 167
Reinforcement Learning: Dense Policy Optimization......Page 169
Q-Learning......Page 171
Sparse Stochastic Policy Optimization......Page 178
Rule Representation......Page 179
Pitt Approach Rule Systems......Page 182
Michigan Approach Learning Classifier Systems......Page 187
Is this Genetic Programming?......Page 196
Random Number Generators, Replicability, and Duplicability......Page 197
Comparing Techniques......Page 198
Boolean Vector Problems......Page 205
Real-Valued Vector Problems......Page 206
Multiobjective Problems......Page 209
Genetic Programming Problems......Page 211
Bibliographies, Surveys, and Websites......Page 215
Publications......Page 217
Tools......Page 218
Conferences......Page 219
Journals......Page 220
Example Course Syllabi for the Text......Page 221
Errata......Page 222
Index......Page 229