Abstract
This paper provides guidelines to design climbers considering a landscape shape under study. In particular, we aim at competing best improvement and first improvement strategies, as well as evaluating the behavior of different neutral move policies. Some conclusions are assessed by an empirical analysis on non-neutral (NK-) and neutral (quantized NK-) landscapes. Experiments show the ability of first improvement to explore rugged landscapes, as well as the interest of accepting neutral moves at each step of the search.
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Basseur, M., Goëffon, A. (2013). Hill-Climbing Behavior on Quantized NK-Landscapes. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_22
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DOI: https://doi.org/10.1007/978-3-642-44973-4_22
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