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Soft Computing

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01.01.2015 | Methodologies and Application

A hybrid selection algorithm for time series modeling

verfasst von: Julie Yu-Chih Liu, Juo-Chiang Hsieh

Erschienen in: Soft Computing | Ausgabe 1/2015

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Abstract

An evolutionary algorithm becomes trapped in local optima when a premature convergence occurs. Research has suggested maintaining population diversity to address this problem. However, traditional methods are excessively complex and time consuming. This study proposes a hybrid selection mechanism in which clonal and roulette wheel selections are alternated to maintain population diversity during evolution. The proposed method is based on a genetic programming technique known as gene expression programming (GEP). The prediction power and efficiency of the proposed method were compared with those of other GEP-based algorithms by using five time series benchmarks. The experimental results indicated that the proposed algorithm outperforms the other algorithms.

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Titel: A hybrid selection algorithm for time series modeling
verfasst von: Julie Yu-Chih Liu
Juo-Chiang Hsieh
Publikationsdatum: 01.01.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1236-6

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