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

Daniel MOLINA, Javier POYATOS, Javier Del SER, Salvador GARCÍA, Hisao ISHIBUCHI, Isaac TRIGUERO*, Bing XUE, Xin YAO, Francisco HERRERA

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

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.
Original languageEnglish
Number of pages17
JournalIEEE Transactions on Evolutionary Computation
Early online date30 Jan 2025
DOIs
Publication statusE-pub ahead of print - 30 Jan 2025

Bibliographical note

Publisher Copyright:
© 1997-2012 IEEE.

Funding

This publication is part of the Project “Ethical, Responsible and General Purpose Artificial Intelligence: Applications In Risk Scenarios” (IAFER) Exp.:TSI-100927-2023-1 funded through the Creation of university-industry research programs (Enia Programs), aimed at the research and development of artificial intelligence, for its dissemination and education within the framework of the Recovery, Transformation and Resilience Plan from the European Union Next Generation EU through the Ministry for Digital Transformation and the Civil Service. This work is also supported by the Knowledge Generation Project PID2023-149128NB-I00. I. Triguero is funded by a Maria Zambrano Senior Fellowship at the University of Granada. J. Del Ser acknowledges funding support from the Basque Government through grants KK2024/00064 and IT1456-22. Xin Yao also acknowledges support from the National Key RD Program of China (Grant No. 2023YFE0106300), and NSFC (Grant No. 62250710682).

Keywords

  • Auto-ML
  • Evolutionary Computation
  • Evolutionary Deep Learning
  • General-purpose AI
  • Neuroevolution
  • Open-ended evolution

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