Parameter cross-validation and early-stopping in univariate marginal distribution algorithm

Authors:
Hao Wu;Jonathan L. Shapiro
Affiliations:
University of Manchester, Manchester, United Kingdom;University of Manchester, Manchester, United Kingdom
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

Citing 3
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Abstract

In this paper, a cross-validation and early-stopping algorithm is devised for parameter updating in the Univariate Marginal Distribution Algorithm (UMDA) to reduce overftting. Our hypothesis is that the well-known problem of diversity loss in UMDA is a consequence of overfitting during the parameter estimation step at each generation. It is tested by experiments on two different optimization problems.