Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74390
Title: Univariate gaussian model for multimodal inseparable problems
Authors: Zhang, G
Li, Y 
Ding, B
Li, Y 
Keywords: Evolutionary computation
Inseparable problem
Random sampling
Univariate model
Issue Date: 2017
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10361, p. 612-623 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: It has been widely perceived that a univariate Gaussian model for evolutionary search can be used to solve separable problems only. This paper explores whether and how the univariate Gaussian model may also be used to solve inseparable problems. The analysis is followed up with experimental tests. The results show that the univariate Gaussian model stipulates no inclination towards separable problems. Further, it is revealed that the model is not only an efficient but also an effective method for solving multimodal inseparable problems. To verify its relative convergence speed, a restart strategy is applied to a univariate Gaussian model (the univariate marginal distribution algorithm) on inseparable problems. The results confirm that the univariate Gaussian model outperforms the five peer algorithms studied in this paper.
Description: 13th International Conference on Intelligent Computing, ICIC 2017, 7 - 10 August 2017
URI: http://hdl.handle.net/10397/74390
ISBN: 9783319633084
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-63309-1_54
Appears in Collections:Conference Paper

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