Title :
A statistical method for global optimization
Author :
Cox, Dennis D. ; John, Susan
Author_Institution :
Dept. of Stat., Illinois Univ., Champaign, IL, USA
Abstract :
An algorithm for finding global optima using statistical prediction is presented. Assuming a random function model, lower confidence bounds on predicted values are used for sequential selection of evaluation points and as a convergence criterion. Comparison with published results for several test functions indicates that the procedure is very efficient in finding the global optimum of a multimodal function, and in terminating with relatively few evaluations
Keywords :
optimisation; search problems; statistical analysis; convergence criterion; evaluation point sequential selection; global optimization; lower confidence bounds; multimodal function; random function model; statistical prediction; Concurrent computing; Convergence; Linear algebra; Optimization methods; Predictive models; Search methods; Statistical analysis; Statistics; Stochastic processes; Testing;
Conference_Titel :
Systems, Man and Cybernetics, 1992., IEEE International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-0720-8
DOI :
10.1109/ICSMC.1992.271617
