Name
Abstract | ||
|---|---|---|
A new memetic algorithm named EAMDGA is designed by combining the characteristics of Environmental Adaption Method for Dynamic Environment (EAMD) and Genetic Algorithm (GA). This algorithm is highly efficient and robust in solving the unimodal and multimodal problems. It avoids the problems of getting trapped in local optima and premature convergence. Performance of this algorithm is checked over a group of 24 unimodal and multimodal benchmark functions provided by Black Box Optimization Benchmarking (BBOB-2013). It is found that EAMDGA is superior in performance in comparison to the other algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/JIFS-16463 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Evolutionary algorithm,adaptive learning,optimization,genetic algorithm,EAMD,phenotypic structure | Memetic algorithm,Mathematical optimization,Cost estimate,Software,Mathematics | Journal |
Volume | Issue | ISSN |
32 | 3 | 1064-1246 |
Citations | PageRank | References |
1 | 0.39 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. K. Mishra | 1 | 3 | 3.45 |
| Ashish Tripathi | 2 | 7 | 2.51 |
| Shailesh Tiwari | 3 | 14 | 6.95 |
| Nitin Saxena | 4 | 280 | 26.72 |