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Binary Growth Optimizer: For Solving Feature Selection Optimization Problems

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Genetic and Evolutionary Computing (ICGEC 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1145))

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Abstract

The present study introduces a novel binary meta-heuristic optimizer, referred to as the Binary Growth Optimizer (BGO), which is specifically developed to address discrete optimization problems. The BGO utilizes transfer functions to convert positions in a continuous space to discrete positions. To assess the performance of the proposed algorithm, we conducted experiments on 14 publicly available datasets from the UCI Machine Learning Repository. We compared the BGO’s performance with seven state-of-the-art meta-heuristics. Based on convergence accuracy statistics, the results indicate that the BGO outperforms the seven compared meta-heuristics, providing the most favorable outcomes.

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Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Shu-Chuan Chu, Zhi-Chao Dou & Jeng-Shyang Pan

  2. Department of Information Management, Chaoyang University of Technology, Taichung, 41349, Taiwan

    Jeng-Shyang Pan

  3. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Lingping Kong

  4. Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 82445, Taiwan

    Tien-Szu Pan

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  1. Shu-Chuan Chu
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  4. Lingping Kong
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  5. Tien-Szu Pan
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Corresponding author

Correspondence to Jeng-Shyang Pan .

Editor information

Editors and Affiliations

  1. Faculty of Automatic Control, Electronics and Computer Science, Department of Distributed Systems and Informatic Devices, Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

  2. Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

    Chin-Shiuh Shieh

  3. National University of Science and Technology, Kaohsiung, Taiwan

    Mong-Fong Horng

  4. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Shu-Chuan Chu

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Chu, SC., Dou, ZC., Pan, JS., Kong, L., Pan, TS. (2024). Binary Growth Optimizer: For Solving Feature Selection Optimization Problems. In: Lin, J.CW., Shieh, CS., Horng, MF., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2023. Lecture Notes in Electrical Engineering, vol 1145. Springer, Singapore. https://doi.org/10.1007/978-981-97-0068-4_31

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