Summary
Memetic algorithms (MAs) with greedy initialization and recombination operators have been successfully applied to several combinatorial optimization problems, including the traveling salesman problem and the graph bipartitioning problem. In this contribution, a k-opt local search heuristic and a greedy heuristic for NK-landscapes are proposed for use in memetic algorithms. The latter is used for the initialization of the population and in a greedy recombination operator. Memetic algorithms with k-opt local search and three different variation operators, including the newly proposed greedy recombination operator, are compared on three types of NK-landscapes. In accordance with the landscape analysis, the MAs with recombination perform better than the MAs with mutation for landscapes with low epistasis. Moreover, the MAs are shown to be superior to previously proposed MAs using 1-opt local search.
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Merz, P. (2005). NK-Fitness Landscapes and Memetic Algorithms with Greedy Operators and k-opt Local Search. In: Hart, W.E., Smith, J.E., Krasnogor, N. (eds) Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32363-5_10
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DOI: https://doi.org/10.1007/3-540-32363-5_10
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