Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910543122530304 |
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| author | Kim, Dae-Won Ko, Song Kang, Bo-Yeong |
| author_facet | Kim, Dae-Won Ko, Song Kang, Bo-Yeong |
| contents | Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms, providing effective and efficient optimization performance in a variety of research areas. Recent studies have proposed new EDAs that employ mutation operators in standard EDAs to increase the population diversity. We present a new mutation operator, a matrix transpose, specifically designed for Bayesian structure learning, and we evaluate its performance in Bayesian structure learning. The results indicate that EDAs with transpose mutation give markedly better performance than conventional EDAs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_18257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning Kim, Dae-Won Ko, Song Kang, Bo-Yeong Neural and Evolutionary Computing Machine Learning Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms, providing effective and efficient optimization performance in a variety of research areas. Recent studies have proposed new EDAs that employ mutation operators in standard EDAs to increase the population diversity. We present a new mutation operator, a matrix transpose, specifically designed for Bayesian structure learning, and we evaluate its performance in Bayesian structure learning. The results indicate that EDAs with transpose mutation give markedly better performance than conventional EDAs. |
| title | Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2407.18257 |