Hibridización entre un Algoritmo Evolutivo y un Algoritmo de Estimación de Distribuciones para la solución de FSSP
Abstract
Los Algoritmos Evolutivos (AEs) son una de las metaheurísticas más ampliamente estudiadas. Éstas, pueden ser
mejoradas en su diseño a fin de realizar una exploración más eficiente del espacio de búsqueda. A su vez, los
algoritmos de Estimación de Distribuciones (EDAs) son una clase de algoritmos basados en el paradigma de
Computación Evolutiva que sustituyen los mecanis mos de variación, utilizados en AEs, por la generación de
individuos generados a través de la información producida de la simulación de una distribución de probabilidad.
El problema de secuenciamiento de Flow Shop (FSSP) ha convocado la atención de muchos investigadores en los
últimos años. Para la resolución del FSSP y con el objetivo de mejorar la eficiencia de la búsqueda como así el
esfuerzo computacional requerido, este trabajo propone un algoritmo híbrido entre estos dos enfoques. Detalles de la
implementación como así las mejoras obtenidas serán discutidas.
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