Abstract
Clustering is one of the important tasks of machine learning. Gene Expression Programming (GEP) is used to solve clustering problems because of its strong global searching ability. In order to solve the limitation of lower rate of convergence and easy falling into optimal local solution in the traditional GEP clustering process, this paper proposes a parallel GEP clustering algorithm based on the producer-consumer model (PGEPC/PCM), which parallelizes the time-consuming operations such as fitness calculation, recombination, and mutation in GEP clustering analysis to speed up, improves the calculation method of fitness function to enable it to cluster automatically. This algorithm can fast calculate accurate clustering center points in parallel. Extensive experiments on four widely used benchmark Iris, Wine, Soybean and Seeds from the UCI machine learning data sets are conducted to investigate the influence of algorithmic component and results are compared with traditional GEP clustering algorithm. These comparisons demonstrate the competitive efficiency of the proposed algorithm.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61573157 and 61703170), Science and Technology Project of Guangdong Province of China (Grant Nos. 2018A0124 and 2017A020224004), Science and Technology Project of Tianhe District of Guangzhou City (Grant No. 201702YG061), Science and technology innovation project for College Students (Grant No. 201910564129). The authors also gratefully acknowledge the reviewers for their helpful comments and suggestions that helped to improve the presentation.
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Yang, L. et al. (2020). A Parallel Gene Expression Clustering Algorithm Based on Producer-Consumer Model. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_8
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