Abstract
Given is a search problem or a sequence of search problems, as well as a set of potentially useful search algorithms. We propose a general framework for online allocation of computation time to search algorithms based on experience with their performance so far. In an example instantiation, we use simple linear extrapolation of performance for allocating time to various simultaneously running genetic algorithms characterized by different parameter values. Despite the large number of searchers tested in parallel, on various tasks this rather general approach compares favorably to a more specialized state-of-the-art heuristic; in one case it is nearly two orders of magnitude faster.
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Gagliolo, M., Zhumatiy, V., Schmidhuber, J. (2004). Adaptive Online Time Allocation to Search Algorithms. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Machine Learning: ECML 2004. ECML 2004. Lecture Notes in Computer Science(), vol 3201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30115-8_15
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DOI: https://doi.org/10.1007/978-3-540-30115-8_15
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