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  • Combinatorial optimization problems form a class of appealing theoretical and practical problems attractive for their complexity and known hardness. They are often NP-hard and as such not solvable by exact methods. Combinatorial optimization problems are subject to numerous heuristic and metaheuristic algorithms, including genetic algorithms. In this paper, we present two new permutation encodings for genetic algorithms and experimentally evaluate the influence of the encodings on the performance and result of genetic algorithm on two synthetic and real-world optimization problems.
  • Combinatorial optimization problems form a class of appealing theoretical and practical problems attractive for their complexity and known hardness. They are often NP-hard and as such not solvable by exact methods. Combinatorial optimization problems are subject to numerous heuristic and metaheuristic algorithms, including genetic algorithms. In this paper, we present two new permutation encodings for genetic algorithms and experimentally evaluate the influence of the encodings on the performance and result of genetic algorithm on two synthetic and real-world optimization problems. (en)
Title
  • Modeling Permutations for Genetic Algorithms
  • Modeling Permutations for Genetic Algorithms (en)
skos:prefLabel
  • Modeling Permutations for Genetic Algorithms
  • Modeling Permutations for Genetic Algorithms (en)
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  • RIV/61989100:27240/09:86075454!RIV11-GA0-27240___
http://linked.open...avai/riv/aktivita
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  • P(GA102/09/1494)
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  • 326792
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  • RIV/61989100:27240/09:86075454
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  • SEARCH; LINEAR ORDERING PROBLEM (en)
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  • [0C761BC9744C]
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  • Malacca, MALAYSIA
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  • NEW YORK
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  • 2009 INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION
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  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
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  • 000277207700018
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  • IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
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  • 978-1-4244-5330-6
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  • 27240
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