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http://hdl.handle.net/2183/29521 Resolución de problemas MaxSAT a través de Evolución Diferencial
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Framil de Amorín, Manuel
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[Resumen] En este proyecto se ha desarrollado un algoritmo capaz de resolver el problema MaxSAT empleando un algoritmo evolutivo híbrido o memético, que combina el algoritmo evolutivo de Evolución Diferencial con GSAT y RandomWalk, dos heurísticas de búsqueda local específicas de MaxSAT. El algoritmo desarrollado ha sido empleado para resolver benchmarks recientes de la Evaluación MaxSAT 2020. Se ha comparado el funcionamiento del algoritmo con el de los mejores solvers presentados en la Evaluación MaxSAT 2020, alcanzando el estado del arte tanto en la calidad de las soluciones como en el tiempo de cómputo requerido para obtenerlas.
[Abstract]In this project, an algorithm capable of solving the MaxSAT problem has been developed using a hybrid evolutionary or memetic algorithm, which combines the evolutionary algorithm of Differential Evolution with GSAT and RandomWalk, two MaxSAT-specific local search heuristics. The algorithm developed has been used to solve recent benchmarks of the MaxSAT Evaluation 2020. The performance of the algorithm has been compared with that of the best solvers presented in the MaxSAT Evaluation 2020, reaching the state of the art both in the quality of the solutions and in the computing time required to obtain them.
[Abstract]In this project, an algorithm capable of solving the MaxSAT problem has been developed using a hybrid evolutionary or memetic algorithm, which combines the evolutionary algorithm of Differential Evolution with GSAT and RandomWalk, two MaxSAT-specific local search heuristics. The algorithm developed has been used to solve recent benchmarks of the MaxSAT Evaluation 2020. The performance of the algorithm has been compared with that of the best solvers presented in the MaxSAT Evaluation 2020, reaching the state of the art both in the quality of the solutions and in the computing time required to obtain them.
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