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Analysis of distributed genetic algorithms for solving cutting problems

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In this paper, a solution to the three-stage two-dimensional cutting problem is presented by using sequential and parallel genetic algorithms (GAs). More specifically, an analysis of including distributed population ideas and parallelism in the basic GA are carried out to solve the problem more accurately and efficiently than with ordinary sequential techniques. Publicly available test problems have been used to illustrate the computational performance of the resulting metaheuristics. Experimental evidence in this work will show that the proposed algorithms outperform their sequential counterparts in time (high speedup with multiprocessors) and numerically (lower number of visited points during the search to find the solutions).
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Keywords: cutting problems; genetic algorithms; parallelism

Document Type: Research Article

Affiliations: 1: Facultad de Ingenieria, Universidad Nacional de La Pampa, Calle 110, esq. 9, 6360 General Pico, La Pampa, Argentina, Email: [email protected] 2: E.T.S.I. Informática, Universidad de Málaga, Campus Teatinos, 29071 Málaga, Españas: , [email protected], Email: [email protected]

Publication date: September 1, 2006

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