open access

https://hdl.handle.net/2115/46847

A genetic algorithm for combinational optimization problems with uncertainties

英語

2010-09-08

IEEE

2010 IEEE International Symposium on Computer-Aided Control System Design (CACSD)

368-373

This paper describes a genetic algorithm (GA) applied to combinational optimization problems in which the objective functions include uncertain constant parameters. In the present method, noises whose probabilistic distribution is assumed based on the problem environments are added to the parameters during the evaluations process. It is assumed that this allows us to make approximate evaluation of the expected values of the objective function under uncertainties. It is shown that the present method results in the robust solutions, which have higher expectation values than the ones obtained by the conventional GA, to the traveling salesman and knapsack problems with uncertainties.

会議発表論文

Accepted Manuscript

© 2010 IEEE. Reprinted, with permission, from Hoshino, K., Igarashi, H., A genetic algorithm for combinational optimization problems with uncertainties , 2010 IEEE International Symposium on Computer-Aided Control System Design (CACSD), Sep. 2010. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Hokkaido University products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.