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A hybrid selection algorithm for time series modeling

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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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Notes

  1. The experimental results of Cobb and Grefenstette (1993) showed that in GA the optima appeared and moved on a periodic basis of every 20 generations.

  2. http://www.stats.uwo.ca/faculty/aim/epubs/mhsets/.

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Acknowledgments

The authors would like to thank the National Science Council, Taiwan, R.O.C. for financially supporting this research under NSC100-2410-H-155-011-MY2.

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Correspondence to Julie Yu-Chih Liu.

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Communicated by Y. Jin.

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Liu, J.YC., Hsieh, JC. A hybrid selection algorithm for time series modeling. Soft Comput 19, 121–131 (2015). https://doi.org/10.1007/s00500-014-1236-6

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