Evolutionary algorithms with clustering for dynamic fitness landscapes
Interest on dynamic multimodal functions risen over the last years since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorith...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2005 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/158250 |
| Acceso en línea: | http://hdl.handle.net/11336/158250 |
| Access Level: | acceso abierto |
| Palabra clave: | DYNAMIC MULTIMODAL FUNCTIONS EVOLUTIONARY ALGORITHMS CLUSTERING ALGORITHMS MACROMUTATION https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | Interest on dynamic multimodal functions risen over the last years since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorithms for dynamic fitness landscapes. In order to mantain diversity in the population they use two clustering techniques and a macromutation operator. Besides, this paper compares two crossover operators: arithmetic and multiparents two points, respectively. Effectiveness and limitations of each algorithm are discuss and analyzed. |
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