Overview and Scope

Generative Models have emerged as key to the field in Artificial Intelligence (AI). In general, a generative model is an AI algorithm that learns the underlying data distribution to produce new distributions, thus generating new data. Evolutionary generative models refer to generative approaches that employ any type of evolutionary algorithm, whether applied on its own or in conjunction with other methods. In a broader sense we can divide evolutionary generative models into at least three main types: 

(i) Evolutionary Computation (EC) as a Generative Model focuses on exploring how EC techniques that serve directly as generative models to produce data, designs, or solutions that fulfill specific criteria or constraints; 

(ii) Generative Models Assisting EC consists in modern generative models, such as Generative Adversarial Networks or diffusion models, that enhance the performance and capabilities of EC methods (e.g., using generative models such as surrogates).  

(iii) EC Assisting Generative Models discusses the role of EC techniques in enhancing generative models themselves, particularly through optimization and exploration. This includes approaches where EC is used to evolve or optimize the parameters of generative networks, help address generative models issues, or introduce adaptive mechanisms that improve model flexibility and resilience. It also delves into topics related to EC population dynamics such as cooperative or adversarial approaches.

The workshop on Evolutionary Generative Models (EGM) aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers focused on generative models in the EC community. Thus, this workshop provides a critical forum for disseminating the experience on the topic using EC as a generative model, generative models assisting EC and EC assisting generative models, presenting new and ongoing research in the field, and to attract new interest from our community.