The convergence analysis and specification of the Population-Based Incremental Learning algorithm

Authors:
Helong Li;Sam Kwong;Yi Hong
Affiliations:
Department of Electronic Commerce, South China University of Technology, Guangzhou 510006, China and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
Venue:
Neurocomputing
Year:
2011

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Abstract

In this paper, we investigate the global convergence properties in probability of the Population-Based Incremental Learning (PBIL) algorithm when the initial configuration p^(^0^) is fixed and the learning rate @a is close to zero. The convergence in probability of PBIL is confirmed by the experimental results. This paper presents a meaningful discussion on how to establish a unified convergence theory of PBIL that is not affected by the population and the selected individuals.