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Conference Papers Year : 2013

A Memetic Approach to Bayesian Network Structure Learning

Abstract

Bayesian networks are graphical statistical models that represent inference between data. For their effectiveness and versatility, they are widely adopted to represent knowledge in different domains. Several research lines address the NP-hard problem of Bayesian network structure learning starting from data: over the years, the machine learning community delivered effective heuristics, while different Evolutionary Algorithms have been devised to tackle this complex problem. This paper presents a Memetic Algorithm for Bayesian network structure learning, that combines the exploratory power of an Evolutionary Algorithm with the speed of local search. Experimental results show that the proposed approach is able to outperform state-of-the-art heuristics on two well-studied benchmarks.

Dates and versions

hal-01221601 , version 1 (28-10-2015)

Identifiers

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Alberto Tonda, Evelyne Lutton, Giovanni Squillero, Pierre-Henri Wuillemin. A Memetic Approach to Bayesian Network Structure Learning. 16th European Conference on Applications of Evolutionary Computation, EvoApplications 2013, Apr 2013, Vienna, Austria. pp.102-111, ⟨10.1007/978-3-642-37192-9_11⟩. ⟨hal-01221601⟩
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