Abstract
In this paper, we present a generic local search algorithm which artificially adds neutrality in search landscapes by discretizing the evaluation function. Some experiments on NK landscapes show that an adaptive discretization is useful to reach high local optima and to launch diversifications automatically. We believe that a hill-climbing using such an adaptive evaluation function could be more appropriated than a classical iterated local search mechanism.
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References
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Acknowledgment
This work was partially supported by the Fondation mathématique Jacques Hadamard within the Gaspard Monge Program for Optimization and operations research.
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Basseur, M., Goëffon, A., Traverson, H. (2015). Exploring Non-neutral Landscapes with Neutrality-Based Local Search. In: Dhaenens, C., Jourdan, L., Marmion, ME. (eds) Learning and Intelligent Optimization. LION 2015. Lecture Notes in Computer Science(), vol 8994. Springer, Cham. https://doi.org/10.1007/978-3-319-19084-6_15
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DOI: https://doi.org/10.1007/978-3-319-19084-6_15
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