Name
Abstract | ||
|---|---|---|
Soft computing methods, and Multi-Objective Evolutionary Algorithms (MOEAs) in particular, lack a general convergence criterion which prevents these algorithms from detecting the generation where further evolution will provide little improvements (or none at all) over the current solution, making them waste computational resources. This paper presents the Least Squares Stopping Criterion (LSSC), an easily configurable and implementable, robust and efficient stopping criterion, based on simple statistical parameters and residue analysis, which tries to introduce as few setup parameters as possible, being them always related to the MOEAs research field rather than the techniques applied by the criterion. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CEC.2010.5586265 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
evolutionary computation,least squares approximations,statistical analysis,MOEA,convergence criterion,least squares stopping criterion,multiobjective evolutionary algorithms,residue analysis,soft computing methods,statistical parameters | Statistical parameter,Convergence (routing),Least squares,Approximation algorithm,Mathematical optimization,Algorithm design,Evolutionary algorithm,Computer science,Evolutionary computation,Artificial intelligence,Soft computing,Machine learning | Conference |
Citations | PageRank | References |
5 | 0.41 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Luis Guerrero | 1 | 18 | 3.79 |
| Luis Martí | 2 | 100 | 7.73 |
| Antonio Berlanga | 3 | 196 | 23.09 |
| Jesús García | 4 | 69 | 7.62 |
| José M. Molina López | 5 | 53 | 12.09 |