Automatic design of machine learning via evolutionary computation: A survey

Machine learning (ML), as the most promising paradigm to discover deep knowledge from data, has been widely applied to practical applications, such as recommender systems, virtual reality, and semantic segmentation. However, building a high-quality ML system for given tasks requires expert knowledge and high computation cost. This poses a significant challenge to the further development of ML in large-scale practical applications. The automatic design of ML has become an increasingly popular research trend. At the same time, evolutionary computation (EC), as an excellent heuristic search technique, has been widely employed in ML optimization, so-called evolutionary machine learning (EML). In this paper, we offer a comprehensive review of the literature (more than 500 references) for EML methods. We first introduce the concepts related to ML and EC. After that, we propose a taxonomy criterion based on the ML and EC perspectives. The important research problems of EML, e.g., ML algorithms, solution representations, search paradigms, acceleration strategies and applications, are reviewed systematically. Lastly, we analyze EML limitations and discuss potential trends that are promising to address in the future.