Name
Title | ||
|---|---|---|
Parameterisation of mutation in evolutionary algorithms using the estimated main effect of genes |
Abstract | ||
|---|---|---|
This work describes how to estimate the main effect of genes in genetic algorithms (GAs). The resulting estimates can not only be used to understand the domination of genes in a GA but also employed to tailor the mutation rate in the GA. A new approach to varying the mutation rate across the representation and over the run of the GA depending on estimates of the main effect of genes is proposed. We demonstrate the use of the proposed method for solving uncapacitated facility location problems. For many well known benchmark problems, the proposed method yields better results than the previously used method. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CEC.2004.1331138 | Evolutionary Computation, 2004. CEC2004. Congress |
Keywords | Field | DocType |
facility location,genetic algorithms,evolutionary algorithms,facility location problems,genes,genetic algorithms,mutation parameterisation,mutation rate | Mathematical optimization,Gene,Evolutionary algorithm,Mutation rate,Computer science,Evolutionary computation,Facility location problem,Artificial intelligence,Main effect,Genetic algorithm,Machine learning,Mutation | Conference |
Volume | ISBN | Citations |
2 | 0-7803-8515-2 | 2 |
PageRank | References | Authors |
0.40 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kit Yan Chan | 1 | 470 | 45.36 |
| Mehmet Emin Aydin | 2 | 62 | 7.04 |
| Terence C. Fogarty | 3 | 51 | 5.74 |