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Structure learning and optimisation in a markov-network based estimation of distribution algorithm

Brownlee, AEI and McCall, JAW and Shakya, SK and Zhang, Q (2009) Structure learning and optimisation in a markov-network based estimation of distribution algorithm. In: UNSPECIFIED, ? - ?.

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Abstract

Structure learning is a crucial component of a multivariate Estimation of Distribution algorithm. It is the part which determines the interactions between variables in the probabilistic model, based on analysis of the fitness function or a population. In this paper we take three different approaches to structure learning in an EDA based on Markov networks and use measures from the information retrieval community (precision, recall and the F-measure) to assess the quality of the structures learned. We then observe the impact that structure has on the fitness modelling and optimisation capabilities of the resulting model, concluding that these results should be relevantto research in both structure learning and fitness modeling. © 2009 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2009 IEEE Congress on Evolutionary Computation, CEC 2009
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 15 Aug 2012 13:32
Last Modified: 17 Aug 2017 18:13
URI: http://repository.essex.ac.uk/id/eprint/1988

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