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A Hybrid “Fast-Slow” Convergent Framework for Genetic Algorithm Inspired by Membrane Computing

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 681))

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Abstract

Genetic algorithm is a well known bio-inspired algorithm, which has been widely used to solve practical problems in real-life. The performance of the algorithm heavily depends on the convergence related to the values of parameters involved. It is formulated as a hard problem to select suitable values of mutation and crossover rates to achieve fast or slow convergence for unknown problems. As a new study of system framework inspired by cell model, membrane computing models is with a membrane structure having region segmentation, intrinsic discrete, non-deterministic, programmable and transparent features. In this paper, a hybrid “fast-slow” convergent framework for genetic algorithm inspired by membrane computing is proposed and applied to search optimal solution of 41 benchmark functions. It is obtained by the data experimental results that our method performs well in solving benchmark functions by achieving accuracy rate about 96%.

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Acknowledgment

This work was supported by National Natural Science Foundation of China (61402187, 61502535, 61572522, 61502063 and 61572523), China Postdoctoral Science Foundation funded project (2016M592267), Program for New Century Excellent Talents in University (NCET-13-1031), 863 Program (2015AA020925), Natural Science Foundation Project of CQ CSTC (No. cstc2012jjA40059), and Fundamental Research Funds for the Central Universities (247201607005A).

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Authors and Affiliations

  1. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, 266580, Shandong, China

    Zhongwei Li, Shengyu Xia, Beibei Sun & Yuezhen Xin

  2. The Center for Bioengineering and Biotechnology, College of Chemical Engineering, China University of Petroleum, Qingdao, 266580, Shandong, China

    Xun Wang

  3. School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing, 400067, China

    Yun Jiang

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  1. Zhongwei Li
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  2. Shengyu Xia
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  3. Yun Jiang
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  4. Beibei Sun
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  5. Yuezhen Xin
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  6. Xun Wang
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Corresponding author

Correspondence to Xun Wang .

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Editors and Affiliations

  1. Xidian University, Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Li, Z., Xia, S., Jiang, Y., Sun, B., Xin, Y., Wang, X. (2016). A Hybrid “Fast-Slow” Convergent Framework for Genetic Algorithm Inspired by Membrane Computing. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_9

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  • DOI: https://doi.org/10.1007/978-981-10-3611-8_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3610-1

  • Online ISBN: 978-981-10-3611-8

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