  In Artificial Intelligence, there is an increasing demand for adaptive models
capable of dealing with a diverse spectrum of learning tasks, surpassing the
limitations of systems devised to cope with a single task. The recent emergence
of General-Purpose Artificial Intelligence Systems (GPAIS) poses model
configuration and adaptability challenges at far greater complexity scales than
the optimal design of traditional Machine Learning models. Evolutionary
Computation (EC) has been a useful tool for both the design and optimization of
Machine Learning models, endowing them with the capability to configure and/or
adapt themselves to the task under consideration. Therefore, their application
to GPAIS is a natural choice. This paper aims to analyze the role of EC in the
field of GPAIS, exploring the use of EC for their design or enrichment. We also
match GPAIS properties to Machine Learning areas in which EC has had a notable
contribution, highlighting recent milestones of EC for GPAIS. Furthermore, we
discuss the challenges of harnessing the benefits of EC for GPAIS, presenting
different strategies to both design and improve GPAIS with EC, covering
tangential areas, identifying research niches, and outlining potential research
directions for EC and GPAIS.
