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A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm.

Nakane, Takumi, Lu, Xuequan and Zhang, Chao 2020, A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm., Computational Intelligence and Neuroscience, vol. 2020, pp. 1-20, doi: 10.1155/2020/8835852.

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Title A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm.
Author(s) Nakane, Takumi
Lu, XuequanORCID iD for Lu, Xuequan orcid.org/0000-0003-0959-408X
Zhang, Chao
Journal name Computational Intelligence and Neuroscience
Volume number 2020
Article ID 8835852
Start page 1
End page 20
Total pages 20
Publisher Hindawi
Place of publication New York, N.Y.
Publication date 2020
ISSN 1687-5265
1687-5273
Summary In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered, and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 15 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed. Also, the induced additional runtime is negligible compared to the total processing time.
Language eng
DOI 10.1155/2020/8835852
Indigenous content off
Field of Research 1109 Neurosciences
1702 Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144381

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.