A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm
Copyright © 2020 Takumi Nakane et al..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2020 |
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| Enthalten in: |
Computational intelligence and neuroscience - 2020(2020) vom: 15., Seite 8835852 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nakane, Takumi [VerfasserIn] |
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| Anmerkungen: |
Date Completed 28.07.2021 Date Revised 29.03.2024 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1155/2020/8835852 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM316314447 |
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| 100 | 1 | |a Nakane, Takumi |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm |
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| 500 | |a Date Revised 29.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Takumi Nakane et al. | ||
| 520 | |a 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 | ||
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| 700 | 1 | |a Zhang, Chao |e verfasserin |4 aut | |
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