Name
Abstract | ||
|---|---|---|
Exploration and exploitation are the two cornerstones which characterize Evolutionary Algorithms (EAs) capabilities. Maintaining the reciprocal balance of the explorative and exploitative power is the key to the success of EA applications. Accordingly, in this work the canonical Genetic Algorithm is augmented by a new mutation scheme that is capable of exploring the unseen regions of the search space, and simultaneously exploiting the already-found promising elements. The proposed mutation operator specifies different mutation rates for different sites (loci) of the individuals. These site-specific rates are wisely derived based on the fitness and structure of the population individuals. In order to retain the balance of the required exploration and exploitation, the mutation rates are adapted during the evolution. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a set of benchmark problems and the outcome is compared with a series of well-known relevant algorithms. The results demonstrate that the newly suggested method significantly outperforms its rivals. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CEC.2010.5586142 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
genetic algorithms,canonical genetic algorithm,evolutionary algorithms,exploitative mutation scheme,explorative mutation scheme,mutation rates,site-specific rates | Population,Mathematical optimization,Algorithm design,Evolutionary algorithm,Mutation rate,Computer science,Evolutionary computation,Artificial intelligence,Probabilistic logic,Genetic algorithm,Benchmark (computing),Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-6909-3 | 6 | 0.47 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatemeh Vafaee | 1 | 64 | 6.48 |
| Peter Nelson | 2 | 20 | 1.51 |