Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects

TL;DR

This survey explores how Evolutionary Computation can be used to design and enhance General-Purpose Artificial Intelligence Systems, highlighting recent advances, challenges, and future research directions in this emerging field.

Contribution

It provides a comprehensive analysis of EC applications in GPAIS, matching properties of GPAIS with EC contributions, and discusses strategies and challenges for future research.

Findings

EC has contributed to the design and optimization of GPAIS.

Recent milestones demonstrate EC's effectiveness in GPAIS development.

Identified research niches and strategies for integrating EC with GPAIS.

Abstract

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.