Paper Info
Title
Simulation optimization using genetic algorithms with optimal computing budget allocation
Abstract
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.
Year
DOI
Venue
2014
10.1177/0037549714548095
Simulation
Keywords
DocType
Volume
optimal computing budget allocation,stochastic optimization,simulation,genetic algorithms,ranking and selection
Journal
90
Issue
ISSN
Citations 
10
0037-5497
1
PageRank 
References 
Authors
0.35
20
2
Name
Order
Citations
PageRank
Hui Xiao110.69
Loo Hay Lee2115993.96