Abstract
The paper investigates the influence of versatile crossover and mutation operators on the efficiency of evolutionary search in solving two important classes of hard optimization problems. Chromosome representations of set partitions and permutations defined in the paper are not problem-oriented and are described together with their versatile variation operators. The proposed representations are tested in evolutionary programs on standard partitions and permutation problems i.e. graph coloring (GCP) and traveling salesman (TSP). The optimization results vary depending on the problem class. They are relatively positive with respect to GCP and negative for TSP.
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Kokosiński, Z. (2005). Effects of Versatile Crossover and Mutation Operators on Evolutionary Search in Partition and Permutation Problems. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_31
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DOI: https://doi.org/10.1007/3-540-32392-9_31
Publisher Name: Springer, Berlin, Heidelberg
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