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|2 23
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|a UAMI
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|a Ergezer, Mehmet.
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|a Evolutionary Computation with Biogeography-Based Optimization.
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| 260 |
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|a Somerset :
|b John Wiley & Sons, Incorporated,
|c 2016.
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| 300 |
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|a 1 online resource (349 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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| 490 |
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|a Computer engineering series. Metaheuristics set ;
|v volume 8
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|a Print version record.
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| 505 |
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|6 880-01
|a Cover; Title Page; Copyright; Contents; 1. The Science of Biogeography; 1.1. Introduction; 1.2. Island biogeography; 1.3. Influence factors for biogeography; 2. Biogeography and Biological Optimization; 2.1. A mathematical model of biogeography; 2.2. Biogeography as an optimization process; 2.3. Biological optimization; 2.3.1. Genetic algorithms; 2.3.2. Evolution strategies; 2.3.3. Particle swarm optimization; 2.3.4. Artificial bee colony algorithm; 2.4. Conclusion; 3. A Basic BBO Algorithm; 3.1. BBO definitions and algorithm; 3.1.1. Migration; 3.1.2. Mutation; 3.1.3. BBO implementation.
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|a 3.2. Differences between BBO and other optimization algorithms3.2.1. BBO and genetic algorithms; 3.2.2. BBO and other algorithms; 3.3. Simulations; 3.4. Conclusion; 4. BBO Extensions; 4.1. Migration curves; 4.2. Blended migration; 4.3. Other approaches to BBO; 4.4. Applications; 4.5. Conclusion; 5. BBO as a Markov Process; 5.1. Markov definitions and notations; 5.2. Markov model of BBO; 5.3. BBO convergence; 5.4. Markov models of BBO extensions; 5.5. Conclusions; 6. Dynamic System Models of BBO; 6.1. Basic notation; 6.2. Dynamic system models of BBO; 6.3. Applications to benchmark problems.
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|a 6.4. Conclusions7. Statistical Mechanics Approximations of BBO; 7.1. Preliminary foundation; 7.2. Statistical mechanics model of BBO; 7.2.1. Migration; 7.2.2. Mutation; 7.3. Further discussion; 7.3.1. Finite population effects; 7.3.2. Separable fitness functions; 7.4. Conclusions; 8. BBO for Combinatorial Optimization; 8.1. Traveling salesman problem; 8.2. BBO for the TSP; 8.2.1. Population initialization; 8.2.2. Migration in the TSP; 8.2.3. Mutation in the TSP; 8.2.4. Implementation framework; 8.3. Graph coloring; 8.4. Knapsack problem; 8.5. Conclusion; 9. Constrained BBO.
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| 505 |
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|a 11. Multi-objective BBO11.1. Multi-objective optimization problems; 11.2. Multi-objective BBO; 11.2.1. Vector evaluated BBO; 11.2.2. Non-dominated sorting BBO; 11.2.3. Niched Pareto BBO; 11.2.4. Strength Pareto BBO; 11.3. Real-world applications; 11.3.1. Warehouse scheduling model; 11.3.2. Optimization of warehouse scheduling; 11.4. Conclusion; 12. Hybrid BBO Algorithms; 12.1. Opposition-based BBO; 12.1.1. Opposition definitions and concepts; 12.1.2. Oppositional BBO; 12.1.3. Experimental results; 12.2. BBO with local search; 12.2.1. Local search methods; 12.2.2. Simulation results.
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| 500 |
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|a 12.3. BBO with other EAs.
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| 504 |
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|a Includes bibliographical references and index.
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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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|a Evolutionary computation.
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| 650 |
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6 |
|a Réseaux neuronaux à structure évolutive.
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| 650 |
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|a Evolutionary computation
|2 fast
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| 700 |
1 |
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|a Simon, Dan.
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| 700 |
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|a Ma, Haiping.
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| 758 |
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|i has work:
|a Evolutionary computation with biogeography-based optimization (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGycM7fCVPkGmytTwqf6Xb
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Ergezer, Mehmet.
|t Evolutionary Computation with Biogeography-Based Optimization.
|d Somerset : John Wiley & Sons, Incorporated, ©2016
|z 9781848218079
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| 830 |
|
0 |
|a Computer engineering series (London, England).
|p Metaheuristics set ;
|v v. 8.
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4790361
|z Texto completo
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| 880 |
8 |
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|6 505-01/(S
|a 9.1. Constrained optimization9.2. Constraint-handling methods; 9.2.1. Static penalty methods; 9.2.2. Superiority of feasible points; 9.2.3. The eclectic evolutionary algorithm; 9.2.4. Dynamic penalty methods; 9.2.5. Adaptive penalty methods; 9.2.6. The niched-penalty approach; 9.2.7. Stochastic ranking; 9.2.8. ε-level comparisons; 9.3. BBO for constrained optimization; 9.4. Conclusion; 10. BBO in Noisy Environments; 10.1. Noisy fitness functions; 10.2. Influence of noise on BBO; 10.3. BBO with re-sampling; 10.4. The Kalman BBO; 10.5. Experimental results; 10.6. Conclusion.
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