Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11807
Title: A real coded population-based incremental learning for inverse problems in continuous space
Authors: Ho, SL 
Zhu, L
Yang, S
Huang, J
Keywords: Evolutionary algorithm (EA)
Inverse problem
Population-based incremental learning (PBIL)
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on magnetics, 2015, v. 51, no. 3, 7093614 How to cite?
Journal: IEEE transactions on magnetics 
Abstract: Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper.
URI: http://hdl.handle.net/10397/11807
ISSN: 0018-9464
EISSN: 1941-0069
DOI: 10.1109/TMAG.2014.2358628
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