Name
Abstract | ||
|---|---|---|
This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving
hard search and optimization problems: a) guided mutation, an offspring generator in which the ideas from EDAs and genetic
algorithms are combined together, we have shown that an evolutionary algorithm with guided mutation outperforms the best GA
for the maximum clique problem, b) evolutionary algorithms refining a heuristic, we advocate a strategy for solving a hard
optimization problem with complicated data structure, and c) combination of two different local search techniques and EDA
for numerical global optimization problems, its basic idea is that not all the new generated points are needed to be improved
by an expensive local search. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s11633-007-0273-3 | International Journal of Automation and Computing |
Keywords | DocType | Volume |
estimation distribution algorithm,global optimization.,memetic algorithms,global optimization,guided mutation,estimation of distribution algorithm,memetic algorithm,distributed algorithm | Journal | 4 |
Issue | ISSN | Citations |
3 | 1751-8520 | 12 |
PageRank | References | Authors |
0.62 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingfu Zhang | 1 | 7634 | 255.05 |
| Jianyong Sun | 2 | 457 | 36.37 |
| Edward Tsang | 3 | 436 | 24.85 |
| sun | 4 | 36 | 2.61 |
| Edward P. K. Tsang | 5 | 899 | 87.77 |
| Edward P. K. Tsang | 6 | 899 | 87.77 |