Pelikan, Martin; Goldberg, David E.; Lobo, Fernando G. A survey of optimization by building and using probabilistic models. (English) Zbl 0988.90052 Comput. Optim. Appl. 21, No. 1, 5-20 (2002). Summary: This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated, and classifies them into a few classes according to the complexity of models they use. Algorithms within each class are briefly described and their strengths and weaknesses are discussed. Cited in 51 Documents MSC: 90C59 Approximation methods and heuristics in mathematical programming 62P15 Applications of statistics to psychology 90C15 Stochastic programming 62B99 Sufficiency and information Keywords:genetic algorithms; model building; decomposable problems; stochastic optimization PDF BibTeX XML Cite \textit{M. Pelikan} et al., Comput. Optim. Appl. 21, No. 1, 5--20 (2002; Zbl 0988.90052) Full Text: DOI OpenURL