Name
Abstract | ||
|---|---|---|
Recent research shows that factorial design methods improve the performance of the crossover operator in evolutionary computation. However the methods employed so far ignore the effects of interaction between genes on fitness, i.e. "epistasis". Here we propose the application of a systematic method for interaction effect analysis to enhance the performance of the crossover operator. It is shown empirically that the proposed method significantly outperforms existing crossover operators on benchmark problems with high interaction between the variables. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-36599-0_3 | EuroGP |
Keywords | Field | DocType |
crossover operator,new factorial design theoretic,parametrical problem,evolutionary computation,factorial design method,high interaction,benchmark problem,systematic method,interaction effect analysis,recent research,evolutionary computing,factorial design,interaction effect | Orthogonal array,Crossover,Evolutionary algorithm,Epistasis,Computer science,Evolutionary computation,Algorithm,Operator (computer programming),Factorial experiment,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
2610 | 0302-9743 | 3-540-00971-X |
Citations | PageRank | References |
4 | 0.54 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kit Yan Chan | 1 | 470 | 45.36 |
| M. Emin Aydin | 2 | 147 | 9.07 |
| T C Fogarty | 3 | 1147 | 152.53 |