Evolutionary Machine Learning meets Self-Supervised Learning: a comprehensive survey
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912687093448704 |
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| author | Vinhas, Adriano Correia, João Machado, Penousal |
| author_facet | Vinhas, Adriano Correia, João Machado, Penousal |
| contents | The number of studies that combine Evolutionary Machine Learning and self-supervised learning has been growing steadily in recent years. Evolutionary Machine Learning has been shown to help automate the design of machine learning algorithms and to lead to more reliable solutions. Self-supervised learning, on the other hand, has produced good results in learning useful features when labelled data is limited. This suggests that the combination of these two areas can help both in shaping evolutionary processes and in automating the design of deep neural networks, while also reducing the need for labelled data. Still, there are no detailed reviews that explain how Evolutionary Machine Learning and self-supervised learning can be used together. To help with this, we provide an overview of studies that bring these areas together. Based on this growing interest and the range of existing works, we suggest a new sub-area of research, which we call Evolutionary Self-Supervised Learning and introduce a taxonomy for it. Finally, we point out some of the main challenges and suggest directions for future research to help Evolutionary Self-Supervised Learning grow and mature as a field. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_07213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evolutionary Machine Learning meets Self-Supervised Learning: a comprehensive survey Vinhas, Adriano Correia, João Machado, Penousal Neural and Evolutionary Computing Machine Learning The number of studies that combine Evolutionary Machine Learning and self-supervised learning has been growing steadily in recent years. Evolutionary Machine Learning has been shown to help automate the design of machine learning algorithms and to lead to more reliable solutions. Self-supervised learning, on the other hand, has produced good results in learning useful features when labelled data is limited. This suggests that the combination of these two areas can help both in shaping evolutionary processes and in automating the design of deep neural networks, while also reducing the need for labelled data. Still, there are no detailed reviews that explain how Evolutionary Machine Learning and self-supervised learning can be used together. To help with this, we provide an overview of studies that bring these areas together. Based on this growing interest and the range of existing works, we suggest a new sub-area of research, which we call Evolutionary Self-Supervised Learning and introduce a taxonomy for it. Finally, we point out some of the main challenges and suggest directions for future research to help Evolutionary Self-Supervised Learning grow and mature as a field. |
| title | Evolutionary Machine Learning meets Self-Supervised Learning: a comprehensive survey |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2504.07213 |