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
