| Description |
1 online resource (261 pages) |
| Contents |
Cover; Title Page; Copyright; Contents; Preface; 1. Evolutionary Algorithms; 1.1. From natural evolution to engineering; 1.2. A generic evolutionary algorithm; 1.3. Selection operators; 1.3.1. Selection pressure; 1.3.2. Genetic drift; 1.3.3. Proportional selection; 1.3.4. Tournament selection; 1.3.5. Truncation selection; 1.3.6. Environmental selection; 1.3.7. Selection operators: conclusion; 1.4. Variation operators and representation; 1.4.1. Generalities about the variation operators; 1.4.2. Crossover; 1.4.3. Mutation; 1.5. Binary representation; 1.5.1. Crossover; 1.5.2. Mutation |
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1.6. The simple genetic algorithm1.7. Conclusion; 2. Continuous Optimization; 2.1. Introduction; 2.2. Real representation and variation operators for evolutionary algorithms; 2.2.1. Crossover; 2.2.2. Mutation; 2.3. Covariance Matrix Adaptation Evolution Strategy; 2.3.1. Method presentation; 2.3.2. The CMA-ES algorithm; 2.4. A restart CMA Evolution Strategy; 2.5. Differential Evolution (DE); 2.5.1. Initializing the population; 2.5.2. The mutation operator; 2.5.3. The crossover operator; 2.5.4. The selection operator; 2.6. Success-History based Adaptive Differential Evolution (SHADE) |
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2.6.1. The algorithm2.6.2. Current-to-pbest/1 mutation; 2.6.3. The success history; 2.7. Particle Swarm Optimization; 2.7.1. Standard Particle Swarm Algorithm 2007; 2.7.2. The parameters; 2.7.3. Neighborhoods; 2.7.4. Swarm initialization; 2.8. Experiments and performance comparisons; 2.8.1. Experiments; 2.8.2. Results; 2.8.3. Discussion; 2.9. Conclusion; 2.10. Appendix: set of basic objective functions used for the experiments; 3. Constrained Continuous Evolutionary Optimization; 3.1. Introduction; 3.1.1. The problem with Constrained Evolutionary Optimization; 3.1.2. Taxonomy |
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3.2. Penalization3.2.1. Static penalties; 3.2.2. Dynamic penalties; 3.2.3. Adaptive penalties; 3.2.4. Self-adaptive penalties; 3.2.5. Stochastic ranking; 3.3. Superiority of feasible solutions; 3.3.1. Special penalization; 3.3.2. Feasibility rules; 3.4. Evolving on the feasible region; 3.4.1. Searching for feasible solutions; 3.4.2. Maintaining feasibility using special operators; 3.5. Multi-objective methods; 3.5.1. Bi-objective techniques; 3.5.2. Multi-objective techniques; 3.6. Parallel population approaches; 3.7. Hybrid methods; 3.8. Conclusion; 4. Combinatorial Optimization |
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4.1. Introduction4.1.1. Solution encoding; 4.1.2. The knapsack problem (KP); 4.1.3. The Traveling Salesman Problem (TSP); 4.2. The binary representation and variation operators; 4.2.1. Binary representation for the 0/1-KP; 4.2.2. Binary representation for the TSP; 4.3. Order-based Representation and variation operators; 4.3.1. Crossover operators; 4.3.2. Mutation operators; 4.3.3. Specific operators; 4.3.4. Discussion; 4.4. Conclusion; 5. Multi-objective Optimization; 5.1. Introduction; 5.2. Problem formalization; 5.2.1. Pareto dominance; 5.2.2. Pareto optimum |
| Notes |
5.2.3. Multi-objective optimization algorithms |
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Print version record |
| Subject |
Genetic algorithms.
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Genetic algorithms.
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| Form |
Electronic book
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| Author |
Ben-Hamida, Sana
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Michalewicz, Zbigniew
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| ISBN |
9781119136415 |
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1119136415 |
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