Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496250
Title: Overfitting in estimation of distribution algorithms (EDAS)
Author: Wu, Hao
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2009
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Abstract:
Estimation of Distribution Algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. They generally build probabilistic models based on good solutions found so far and use the constructed models to guide the further search. There is a significant problem within EDAs: when the sample size of EDAs at each generation is not big enough, EDAs fail to find the global optimum no matter how long they are run. To find out the reason, we noticed one of the most important phenomena concerning machine leaming from data is overfitting. in this, the learning algorithm adapts so well to the given data, that noise or particularities of the specific sample are also encoded by the learned model. lt results in reduced performance when the task is the generalisation to unseen data, as producing an overly complex model which may consume unnecessary learning time and computational resources.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.496250  DOI: Not available
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