Name
Title | ||
|---|---|---|
Tracking Extrema in Dynamic Fitness Functions with Dissortative Mating Genetic Algorithms |
Abstract | ||
|---|---|---|
This paper investigates the behavior of the Adaptive Dissortative Mating Genetic Algorithm (ADMGA) on dynamic problems and compares it with other Genetic Algorithms (GA). ADMGA is a non-random mating algorithm that selects parents according to their Hamming distance, via a self-adjustable threshold value. The resulting method, by keeping population diversity during the run, provides new means for GAs to deal with dynamic problems, which demand high diversity in order to track the optima. Tests conducted on combinatorial and trap functions indicate that ADMGA is more robust than traditional GAs and it is capable of outperforming a previously proposed dis-sortative scheme on a wide range of tests. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/HIS.2008.52 | Barcelona |
Keywords | Field | DocType |
genetic algorithms,adaptive dissortative mating genetic,hamming distance,dis-sortative scheme,dynamic fitness functions,new mean,non-random mating algorithm,tracking extrema,high diversity,dissortative mating genetic algorithms,dynamic problem,traditional gas,population diversity,genetic algorithm,fitness function,frequency diversity,steady state,gallium,optimization | Mathematical optimization,Diversity scheme,Computer science,Maxima and minima,Population diversity,Hamming distance,Dynamic problem,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
978-0-7695-3326-1 | 3 | 0.37 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. M. Fernandes | 1 | 3 | 0.37 |
| J. J. Merelo | 2 | 363 | 33.51 |
| A. C. Rosa | 3 | 15 | 2.37 |