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|a 519.7
|2 23
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|a UAMI
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|a Petrowski, Alain.
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|a Evolutionary Algorithms :
|b an Overview.
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| 260 |
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|a Somerset :
|b John Wiley & Sons, Incorporated,
|c 2017.
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| 300 |
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|a 1 online resource (261 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Print version record.
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|a 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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|a 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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|a 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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|a 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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| 505 |
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|a 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.
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| 500 |
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|a 5.2.3. Multi-objective optimization algorithms.
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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0 |
|a Genetic algorithms.
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| 650 |
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6 |
|a Algorithmes génétiques.
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| 650 |
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7 |
|a Genetic algorithms
|2 fast
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| 700 |
1 |
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|a Ben-Hamida, Sana.
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| 700 |
1 |
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|a Michalewicz, Zbigniew.
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| 758 |
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|i has work:
|a Evolutionary algorithms (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGgk8VpMgPRjvBQ9QmJPw3
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Petrowski, Alain.
|t Evolutionary Algorithms : An Overview.
|d Somerset : John Wiley & Sons, Incorporated, ©2017
|z 9781848218048
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4841876
|z Texto completo
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| 938 |
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|a EBL - Ebook Library
|b EBLB
|n EBL4841876
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| 994 |
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|a 92
|b IZTAP
|
