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Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in fighting uncertainty with uncertainty. This book discusses theoretical aspects of many such algorithms and covers their application in various scientific fields.
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Keywords
- applied mathematics
- mathematical modelling
- Mathematics
- Mathematics & science
- thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
Links
DOI: 10.5772/623Editions