A survey of random methods for parameter optimization

A class of algorithms known as random search methods has been developed for obtaining solutions to para meter optimization problems. This paper provides a guide to the literature in this area, while describ ing some of the theoretical results obtained as well as the development of practical algorithms. Included are brief descriptions of the problems associated with inequality constraints, noisy measurements, and the location of the global optimum. An attempt is made to indicate types of problems for which random search methods are especially attractive.