Simulation optimization using genetic algorithms with optimal computing budget allocation. (October 2014)

• Record Type: Journal Article
• Authors: Xiao, Hui; Lee, Loo Hay
• Is Part Of: Simulation. Volume 90:Number 10(2014:Oct.)
• Page Start: 1146
• Page End: 1157
• Publication Date: 2014-10
• Subjects: Ranking and selection -- simulation -- genetic algorithms -- optimal computing budget allocation -- stochastic optimization; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549714548095
• Languages: English
• ISSNs: 0037-5497

Abstract

A method is proposed to improve the efficiency of simulation optimization by integrating the notion of optimal computing budget allocation into the genetic algorithm, which is a global optimization search method that iteratively generates new solutions using elite candidate solutions. When applying genetic algorithms in a stochastic setting, each solution must be simulated a large number of times. Hence, the computing budget allocation can make a significant difference to the performance of the genetic algorithm. An easily implementable closed-form computing budget allocation rule of ranking the best m solutions out of total k solutions is proposed. The proposed budget allocation rule can perform better than the existing asymptotically optimal allocation rule for ranking the best m solutions. By integrating the proposed budget allocation rule, the search efficiency of genetic algorithms has significantly improved, as shown in the numerical examples.