 Simulation optimization by genetic searchJames M. Yunker and Jeffrey D. Tew Mathematics and Computers in Simulation (MATCOM), 1994, vol. 37, issue 1, 17-28 Abstract: This is an example of simulation optimization by genetic search. We compare a new technique, genetic search, to two old techniques the pattern search and the response surface method search. The pattern search uses the Hooke–Jeeves algorithm and the response surface method search uses the computer code of Dennis Smith. This research compares these three algorithms for accuracy and stability. In accuracy we look at how close the algorithm comes to the optimum. The optimum having been previously determined from exhaustive testing. We evaluate stability using the variance of the response function determined from 50 searches; the lower the variance the more stable the response. The example tested is a university time-sharing computer system with two real decision variables: quantum, the amount of time that the computer spends on a job before sending it back to the queue and overhead, that is the time that its takes to execute this routing operation. The response is the cost of operating the system determined from a cost function. Keywords: Simulation; Simulation optimization; Genetic algorithms; Artificial intelligence (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:37:y:1994:i:1:p:17-28 DOI: 10.1016/0378-4754(94)90055-8 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.