Abstract
In recent years, the quantum genetic algorithm is drawing attention from scholars. The algorithm is a probabilistic optimization method of combining the quantum computing and genetic algorithms. How to design a more effective way to improve the performance of the quantum genetic algorithm is more worth studying. As is known to all, the P system can search for the optimal clustering partition with the help of its parallel computing advantage effectively. In this paper, we attempt to utilize the P system to optimize the quantum genetic algorithm (PQGA) and then to alleviate the drawbacks in the k-means clustering method. The algorithm can improve the parallelism of the quantum genetic algorithm and short the average running time of the algorithm effectively. This algorithm is of particular interests to when dealing with large and heterogeneous data sets and when being faced with an unknown number of clusters, which due to that it can obtain the optimal number of clusters as well as providing the optimal cluster centroids. In this paper, we use the real datasets in UCI to validate the performance of PQGA. The experimental results show that PQGA is promising and effective.
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References
Farag, M.A., El-Shorbagy, M.A., El-Desoky, I.M., et al.: Genetic algorithm based on k-means clustering technique for multi-objective resource allocation problems. Br. J. Appl. Sci. Technol. 8, 80–96 (2015)
Liang, C.Y., Bai, H., Cai, M.J., et al.: Advances in quantum genetic algorithm. Jisuanji Yingyong Yanjiu 29(7), 2401–2405 (2012)
Paun, G.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)
Escuela, G., Gutierrez-Naranjo, M.A.: An application of genetic algorithms to membrane computing. In: Proceedings of Eighth Brainstorming Week on Membrane Computing, pp 101–108 (2010)
Jiang, Y., Peng, H., Huang, X., et al.: A novel clustering algorithm based on P systems. Int. J. Innov. Comput. Inf. Control 10(2), 753–765 (2014)
Wang, K., Wang, N.: A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil. Chem. Eng. J. 167(1), 228–239 (2011)
Sardana, M., Agrawal, R.K., Kaur, B.: Clustering in conjunction with quantum genetic algorithm for relevant genes selection for cancer microarray data. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013 Workshops. LNCS, vol. 7867, pp. 428–439. Springer, Heidelberg (2013)
Zhou, R., Cao, J.: Quantum novel genetic algorithm based on parallel subpopulation computing and its application. Artif. Intell. Rev. 41(3), 359–371 (2014)
Acknowledgment
Project supported by National Natural Science Foundation of China (61170038, 61472231, 61502283), Jinan City independent innovation plan project in Colleges and Universities, China (201401202), Ministry of education of Humanities and social scienc-e research project, China (12YJA630152), Social Science Fund Project of Shandong Province, China (11CGLJ22).
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Hou, C., Liu, X. (2016). P System Based Quantum Genetic Algorithm to Solve the Problem of Clustering. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_60
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DOI: https://doi.org/10.1007/978-3-319-31854-7_60
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