Evolutionary Generative Models

In the last decade, generative models have seen widespread use for their ability to generate diverse artefacts in an increasingly simple way. Historically, the use of evolutionary computation as a generative model approach was dominant, and recently, as a consequence of the rise in popularity and amount of research being conducted in artificial intelligence, the application of evolutionary computation to generative models has broadened its scope to encompass more complex machine learning approaches. Therefore, it is opportune to propose a term capable of accommodating all these models under the same umbrella. To address this, we propose the term evolutionary generative modelsEvolutionary generative model to refer to generative approaches that employ any type of evolutionary algorithm, whether applied on its own or in conjunction with other methods. In particular, we present a literature review on this topic, identifying the main properties of evolutionary generative modelsEvolutionary generative model and categorising them into four different categories: evolutionary computation without machine learning, evolutionary computation aided by machine learning, machine learning aided by evolutionary computation and machine learning evolved by evolutionary computation. Therefore, we systematically analyse a selection of prominent works concerning evolutionary generative modelsEvolutionary generative model. We conclude by addressing the most relevant challenges and open problems faced by current evolutionary generative modelsEvolutionary generative model and discussing where the topic’s future is headed.