Abstract
In this paper, we describe our work on Estimation of Distribution Algorithms (EDAs) that address sequencing problems, i.e., the task of finding the best ordering of a set of items or an optimal schedule to perform a given set of operations. Specifically, we focus on the use of probabilistic models that are based on n-gram statistics. These models have been used extensively in modeling statistical properties of sequences. We start with an EDA that uses a bigram model, then extend this scheme to higher-order models. However, directly replacing the bigram model with a higher-order model often results in premature convergence. We give an explanation on why this is the case along with some empirical support for our intuition. Following that, we propose a technique that combines multiple models of different orders, which allows for smooth transition from lower-order models to higher-order ones. Furthermore, this technique can also be used to incorporate other heuristics and prior knowledge about the problem into the search mechanism.
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Chuang, CY., Smith, S.F. (2014). Evolving Mixtures of n-gram Models for Sequencing and Schedule Optimization. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_31
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DOI: https://doi.org/10.1007/978-3-319-10762-2_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10761-5
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