A study on the global convergence time complexity of estimation of distribution algorithms

Authors:
R. Rastegar;M. R. Meybodi
Affiliations:
Computer Engineering Department, Amirkabir University, Tehran, Iran;Computer Engineering Department, Amirkabir University, Tehran, Iran
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

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Abstract

The Estimation of Distribution Algorithm is a new class of population based search methods in that a probabilistic model of individuals are estimated based on the high quality individuals and used to generate the new individuals. In this paper we compute 1) some upper bounds on the number of iterations required for global convergence of EDA 2) the exact number of iterations needed for EDA to converge to global optima.