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Description
  • Linear ordering problem (LOP) is a well know NP-hard optimization problem attractive for its complexity, rich collection of testing data and variety of real world applications. It is also a popular benchmark For novel optimization and metaheuristic algorithms. In this paper, we compare the performance of genetic algorithms and differential evolution as efficient metaheuristic solvers of the LOP.
  • Linear ordering problem (LOP) is a well know NP-hard optimization problem attractive for its complexity, rich collection of testing data and variety of real world applications. It is also a popular benchmark For novel optimization and metaheuristic algorithms. In this paper, we compare the performance of genetic algorithms and differential evolution as efficient metaheuristic solvers of the LOP. (en)
Title
  • Differential Evolution and Genetic Algorithms for the Linear Ordering Problem
  • Differential Evolution and Genetic Algorithms for the Linear Ordering Problem (en)
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  • Differential Evolution and Genetic Algorithms for the Linear Ordering Problem
  • Differential Evolution and Genetic Algorithms for the Linear Ordering Problem (en)
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  • RIV/61989100:27240/09:00020978!RIV10-GA0-27240___
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  • P(GA102/09/1494)
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  • 310492
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  • RIV/61989100:27240/09:00020978
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  • linear ordering problem; genetic algoritmhs; differential evolution (en)
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  • [2A66E8409B27]
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  • Santiago, CHILE
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  • Berlín
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  • Knowledge-Based and Intelligent Information and Engineering Systems
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  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
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  • 000273270800017
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  • Springer Heidelberg
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  • 978-3-642-04594-3
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  • 27240
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