Noisy Optimization With Evolution Strategies:
Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for num...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer US
2002
|
| Ausgabe: | 1st ed. 2002 |
| Schriftenreihe: | Genetic Algorithms and Evolutionary Computation
8 |
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise. Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation. This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms. Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms |
| Beschreibung: | 1 Online-Ressource (IX, 158 p) |
| ISBN: | 9781461511052 |
| DOI: | 10.1007/978-1-4615-1105-2 |
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| spelling | Arnold, Dirk V. Verfasser aut Noisy Optimization With Evolution Strategies by Dirk V. Arnold 1st ed. 2002 New York, NY Springer US 2002 1 Online-Ressource (IX, 158 p) txt rdacontent c rdamedia cr rdacarrier Genetic Algorithms and Evolutionary Computation 8 Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise. Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation. This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms. Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms Artificial Intelligence Optimization Theory of Computation Artificial intelligence Mathematical optimization Computers Erscheint auch als Druck-Ausgabe 9781402071058 Erscheint auch als Druck-Ausgabe 9781461353973 Erscheint auch als Druck-Ausgabe 9781461511069 https://doi.org/10.1007/978-1-4615-1105-2 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Arnold, Dirk V. Noisy Optimization With Evolution Strategies Artificial Intelligence Optimization Theory of Computation Artificial intelligence Mathematical optimization Computers |
| title | Noisy Optimization With Evolution Strategies |
| title_auth | Noisy Optimization With Evolution Strategies |
| title_exact_search | Noisy Optimization With Evolution Strategies |
| title_exact_search_txtP | Noisy Optimization With Evolution Strategies |
| title_full | Noisy Optimization With Evolution Strategies by Dirk V. Arnold |
| title_fullStr | Noisy Optimization With Evolution Strategies by Dirk V. Arnold |
| title_full_unstemmed | Noisy Optimization With Evolution Strategies by Dirk V. Arnold |
| title_short | Noisy Optimization With Evolution Strategies |
| title_sort | noisy optimization with evolution strategies |
| topic | Artificial Intelligence Optimization Theory of Computation Artificial intelligence Mathematical optimization Computers |
| topic_facet | Artificial Intelligence Optimization Theory of Computation Artificial intelligence Mathematical optimization Computers |
| url | https://doi.org/10.1007/978-1-4615-1105-2 |
| work_keys_str_mv | AT arnolddirkv noisyoptimizationwithevolutionstrategies |