Modeling Permutations for Genetic Algorithms

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 metaheurist...

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Bibliographic Details
Published in2009 International Conference of Soft Computing and Pattern Recognition pp. 100 - 105
Main Authors Kromer, P., Platos, J., Snasel, V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
Subjects
Computer science
Encoding
Genetic algorithms
Genetic mutations
Genetic programming
Heuristic algorithms
Optimization methods
Pattern recognition
permutation
Testing
Traveling salesman problems
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Summary: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.
ISBN:1424453305
9781424453306
DOI:10.1109/SoCPaR.2009.31