Abstract
Estimation of Distribution Algorithms (EDAs) are a kind of evolutionary algorithms where classical genetic operators are replaced by the estimation of a probabilistic model and its simulation in order to generate the next population. With this work we tackle, in a preliminary way, how to incorporate specific heuristic knowledge in the sampling phase. We propose two ways to do it and evaluate them experimentally through the classical knapsack problem.
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de la Ossa, L., Gámez, J.A., Puerta, J.M. (2004). Heuristic Based Sampling in Estimation of Distribution Algorithms: An Initial Approach. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_38
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DOI: https://doi.org/10.1007/978-3-540-25945-9_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22218-7
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