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  • Linear Ordering Problem (LOP) is a well know optimization problem attractive for its complexity (it is a NP-hard problem), rich collection of testing data and variety of real world applications. In this paper, we investigate the usage and performance of two variants of Genetic Algorithms - Mutation Only Genetic Algorithms and Higher Level Chromosome Genetic Algorithms - on the Linear Ordering Problem. Both methods are tested and evaluated on a collection of real world and artificial LOP instances.
  • Linear Ordering Problem (LOP) is a well know optimization problem attractive for its complexity (it is a NP-hard problem), rich collection of testing data and variety of real world applications. In this paper, we investigate the usage and performance of two variants of Genetic Algorithms - Mutation Only Genetic Algorithms and Higher Level Chromosome Genetic Algorithms - on the Linear Ordering Problem. Both methods are tested and evaluated on a collection of real world and artificial LOP instances. (en)
Title
  • A Study on Performance of MOGA and HLCGA for the Linear Ordering Problem
  • A Study on Performance of MOGA and HLCGA for the Linear Ordering Problem (en)
skos:prefLabel
  • A Study on Performance of MOGA and HLCGA for the Linear Ordering Problem
  • A Study on Performance of MOGA and HLCGA for the Linear Ordering Problem (en)
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  • RIV/61989100:27240/09:00020944!RIV10-GA0-27240___
http://linked.open...avai/riv/aktivita
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  • P(GA102/09/1494)
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  • 301610
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  • RIV/61989100:27240/09:00020944
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  • Genetic algorithms; Optimization methods (en)
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  • [5AAF2F491FD3]
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  • Muroran, JAPAN
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  • NEW YORK
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  • IEEE CONFERENCE ON SOFT COMPUTING IN INDUSTRIAL APPLICATIONS SMCIA/08
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  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
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  • 000272228200072
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  • IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
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  • 978-1-4244-3782-5
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  • 27240
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