ON SEARCH TECHNIQUES IN ADAPTIVE SYSTEMS,
Abstract
The study is concerned with the problem of optimizing performance of a system with respect to a set of parameters. The mathematical relation between these parameters and the system performance is unknown so that indirect optimization method are not applicable. It is assumed that the system performance can be determined, at least approximately, for any set of the parameters. The convergence properties of several direct search optimization methods are studied experimentally and ways in these convergence properties can be improved are presented. The adaptive random optimization method is modified to improve its convergence properties for application to unimodal surfaces. The convergence properties of this method are compared to those of the stochastic approximation method. The adaptive random optimization method and the stochastic automaton method are modified to improve their convergence properties for application to multimodal surfaces. The convergence properties of these methods are compared to those of the standard stochastic automaton method, the concurrent global and local search method and the multimodal stochastic approximation method. Pattern recognition techniques are used to extend the applicability of the adaptive random optimization method and the stochastic automaton method to switching environments. The convergence properties of these methods are compared to those of the stochastic automaton method without pattern recognition. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1970
- Accession Number
- AD0711098
Entities
People
- King Sun Fu
- Larry D. Cockrell
Organizations
- Purdue University