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A novel local exploitation scheme for conditionally breeding real-coded genetic algorithm

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Abstract

Conditionally breeding real-coded genetic algorithm (CGAR) is effective approach for continue domain problems, in which crossover and mutation behaviors are performed by difference-degree between individuals instead of given probability. In this paper we present a novel exploitation scheme for CGAR to balance between two contradictory aspects of its performance: exploration and exploitation, which is aimed at improving its ability to converge to the near-optimal solutions. The proposed algorithms are evaluated on a number of benchmark functions and the simulation results show that the proposed algorithm performs quite well and outperforms other algorithms.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Yang Zhao & Yanguang Cai

  2. College of Instrumentation & Electrical Engineering, Jilin University, Changchun, 130000, China

    Defu Cheng

Authors
  1. Yang Zhao
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  2. Yanguang Cai
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  3. Defu Cheng
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Correspondence to Defu Cheng.

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Zhao, Y., Cai, Y. & Cheng, D. A novel local exploitation scheme for conditionally breeding real-coded genetic algorithm. Multimed Tools Appl 76, 17955–17969 (2017). https://doi.org/10.1007/s11042-016-3493-0

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  • DOI: https://doi.org/10.1007/s11042-016-3493-0

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