Stochastic Optimization: a Review

Fouskakis, D; Draper, D

dc.contributor.author	Fouskakis, D	en
dc.contributor.author	Draper, D	en
dc.date.accessioned	2014-03-01T01:50:58Z
dc.date.available	2014-03-01T01:50:58Z
dc.date.issued	2001	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/26189
dc.relation.uri	http://www.math.ntua.gr/~fouskakis/Publications/4.pdf	en
dc.subject	bayesian decision theory	en
dc.subject	Cost Effectiveness	en
dc.subject	Exact Algorithm	en
dc.subject	Expected Utility	en
dc.subject	General Linear Model	en
dc.subject	Genetic Algorithm	en
dc.subject	Global Optimization	en
dc.subject	Heuristic Method	en
dc.subject	Hospital Care	en
dc.subject	Hybrid Algorithm	en
dc.subject	Simulated Annealing	en
dc.subject	Stochastic Optimization	en
dc.subject	Variable Selection	en
dc.subject	Local Search	en
dc.subject	tabu search	en
dc.title	Stochastic Optimization: a Review	en
heal.type	journalArticle	en
heal.publicationDate	2001	en
heal.abstract	Summary We review three leading stochastic optimization methods—simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, detail advantages and disadvantages, and summarize the literature on optimal values of the inputs. As a motivating example we describe the solution—using Bayesian decision theory, via maximization of expected utility—of a variable selection problem in	en