Abstract
Iterated local search is a simple yet powerful metaheuristic. It is only drawback is that it is quite sensitive to its only parameter: the perturbation step size. Adaptive operator selection methods are on-line adaptive algorithms that adjust the probability of applying the search operators to the current solutions. In this short note, we show the use of the adaptive pursuit algorithm to automatically select the perturbation step size for ILS when optimizing a blind, single-constraint knapsack problem. The resulting adaptive ILS achieves almost the same performance as the ILS with the best perturbation step size but without the need to determine the optimal parameter setting.
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Lourenço, H., Martin, O., Stützle, T.: A beginner’s introduction to iterated local search. In: Proceedings of the 4th Metaheuristics International Conference (2001)
DaCosta, L., Fialho, A., Schoenauer, M., Sebag, M.: Adaptive operator selection with dynamic multi-armed bandit. In: Proceedings of the 10th Genetic and Evolutionary Computation Conference, pp. 913–920 (2008)
Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of the 7th Genetic and Evolutionary Computation Conference, pp. 1539–1546 (2005)
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© 2009 Springer-Verlag Berlin Heidelberg
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Thierens, D. (2009). Adaptive Operator Selection for Iterated Local Search. In: Stützle, T., Birattari, M., Hoos, H.H. (eds) Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2009. Lecture Notes in Computer Science, vol 5752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03751-1_15
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DOI: https://doi.org/10.1007/978-3-642-03751-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03750-4
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