Name
Abstract | ||
|---|---|---|
We present a new nature-inspired algorithm, mt-GA, which is a parallelized version of a simple GA, where subpopulations evolve independently from each other and on different threads. The overall goal is to develop a population-based algorithm capable to escape from local optima. In doing so, we used complex trap functions, and we provide experimental answers to some crucial implementation decision problems. The obtained results show the robustness and efficiency of the proposed algorithm, even when compared to well-known state-of-the art optimization algorithms based on the clonal selection principle. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-01692-4_11 | Studies in Computational Intelligence |
Keywords | Field | DocType |
Genetic algorithms,multi-threaded genetic algorithms,trap functions,toy problems,global optimization,optimization | Population,Decision problem,Global optimization,Computer science,Local optimum,Parallel computing,Thread (computing),Robustness (computer science),Clonal selection,Genetic algorithm | Conference |
Volume | ISSN | Citations |
512 | 1860-949X | 1 |
PageRank | References | Authors |
0.39 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| vincenzo cutello | 1 | 553 | 57.63 |
| Angelo G. De Michele | 2 | 1 | 0.39 |
| Mario Pavone | 3 | 212 | 19.41 |