A survey of optimization by building and using probabilistic models. (English) Zbl 0988.90052

Pelikan, Martin; Goldberg, David E.; Lobo, Fernando G.

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.

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