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Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm

Brownlee, AEI and McCall, JAW and Zhang, Q and Brown, DF (2008) Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm. In: UNSPECIFIED, ? - ?.

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Selection is one of the defining characteristics of an evolutionary algorithm, yet inherent in the selection process is the loss of some information from a population. Poor solutions may provide information about how to bias the search toward good solutions. Many Estimation of Distribution Algorithms (EDAs) use truncation selection which discards all solutions below a certain fitness, thus losing this information. Our previous work on Distribution Estimation using Markov networks (DEUM) has described an EDA which constructs a model of the fitness function; a unique feature of this approach is that because selective pressure is built into the model itself selection becomes optional. This paper outlines a series of experiments which make use of this property to examine the effects of selection on the population. We look at the impact of selecting only highly fit solutions, only poor solutions, selecting a mixture of highly fit and poor solutions, and abandoning selection altogether. We show that in some circumstances, particularly where some information about the problem is already known, selection of the fittest only is suboptimal. © 2008 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: 2008 IEEE Congress on Evolutionary Computation, CEC 2008
Subjects: Q Science > QA Mathematics
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: 13 Jan 2012 11:55
Last Modified: 17 Oct 2019 21:15

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