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Memetic Algorithms for the MinLA Problem
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Fecha:
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2006
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Tipo:
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Memoria Congreso
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Titulo:
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Memetic Algorithms for the MinLA Problem
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Autores:
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HAO Jin-Kao.
TORRES Jose.
RODRIGUEZ Eduardo.
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Proyecto:
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País:
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CONJUNTO
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Congreso:
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7th International Conference, Evolution Artificielle
, (EA 2005)
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Páginas:
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73-84 Pp.
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Editorial:
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Springer Berlin / Heidelberg
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ISBN:
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978-3-540-33589-4
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Enlace:
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Descripción:
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This paper presents a new Memetic Algorithm designed to compute near optimal solutions for the MinLA problem. It incorporates a highly specialized crossover operator, a fast MinLA heuristic used to create the initial population and a local search operator based on a fine tuned Simulated Annealing algorithm. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms.
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