An ant-based rule for UMDA's update strategy

Authors:
C. M. Fernandes;C. F. Lima;J. L. J. Laredo;A. C. Rosa;J. J. Merelo
Affiliations:
Department of Computer's Architecture and Technology, University of Granada, Spain and LaSEEB-ISR-IST, Technical Univ. of Lisbon, Portugal;Informatics Lab., University of Algarve, Faro, Portugal;Department of Computer's Architecture and Technology, University of Granada, Spain;LaSEEB-ISR-IST, Technical Univ. of Lisbon, Portugal;Department of Computer's Architecture and Technology, University of Granada, Spain
Venue:
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Year:
2009

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Abstract

This paper investigates an update strategy for the Univariate Marginal Distribution Algorithm (UMDA) probabilistic model inspired by the equations of the Ant Colony Optimization (ACO) computational paradigm. By adapting ACO's transition probability equations to the univariate probabilistic model, it is possible to control the balance between exploration and exploitation by tuning a single parameter. It is expected that a proper balance can improve the scalability of the algorithm on hard problems with bounded difficulties and experiments conducted on such problems with increasing difficulty and size confirmed these assumptions. These are important results because the performance is improved without increasing the complexity of the model, which is known to have a considerable computational effort.