Improved MaxiMin Selection for Well Spread Pareto Fronts
An algorithm to achieve maximal spread and almost perfectly distributed Pareto fronts is presented. The MaxiMin algorithm add points to the archive of selected individuals one by one, each point which is added maximizes the distance from the current selected points. This method is independent of the...
| Autor: | |
|---|---|
| Formato: | informe técnico |
| Estado: | Versión publicada |
| Fecha de publicación: | 2007 |
| País: | México |
| Recursos: | Centro de Investigación en Matemáticas |
| Repositorio: | Repositorio Institucional CIMAT |
| Idioma: | inglés |
| OAI Identifier: | oai:cimat.repositorioinstitucional.mx:1008/630 |
| Acesso em linha: | http://cimat.repositorioinstitucional.mx/jspui/handle/1008/630 |
| Access Level: | acceso abierto |
| Palavra-chave: | info:eu-repo/classification/MSC/Algorítmos info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120302 |
| Resumo: | An algorithm to achieve maximal spread and almost perfectly distributed Pareto fronts is presented. The MaxiMin algorithm add points to the archive of selected individuals one by one, each point which is added maximizes the distance from the current selected points. This method is independent of the evolutionary operators used to perform the search. This work explains how to combine the MaxiMin selection with state of the art multi-objective algorithms, such as NSGA-II, SPEA2, and DEMO. Experiments were ran with and without MaxiMin for comparison purposes. ε -MOEA is used as reference. Performance metrics and graphical results are shown for comparison. |
|---|