Evolutionary Machine Learning meets Self-Supervised Learning: a comprehensive survey
It identifies a gap in literature for researchers interested in automating machine learning design and reducing reliance on labeled data, but it is incremental as it synthesizes existing works without new empirical results.
This survey addresses the lack of reviews on combining Evolutionary Machine Learning and self-supervised learning, proposing a new sub-area called Evolutionary Self-Supervised Learning with a taxonomy and future research directions.
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.