A1 Journal article (refereed)
Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method (2024)


Misitano, G. (2024). Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method. ACM Transactions on Evolutionary Learning and Optimization, 4(1), 1-39. https://doi.org/10.1145/3626104


JYU authors or editors


Publication details

All authors or editors: Misitano, Giovanni

Journal or series: ACM Transactions on Evolutionary Learning and Optimization

ISSN: 2688-299X

eISSN: 2688-3007

Publication year: 2024

Publication date: 28/09/2023

Volume: 4

Issue number: 1

Pages range: 1-39

Article number: 4

Publisher: Association for Computing Machinery (ACM)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1145/3626104

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/93374


Abstract

Multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. The solutions to these problems are known as Pareto optimal solutions, which are mathematically incomparable. Thus, a decision maker must be employed to provide preferences to find the most preferred solution. However, decision makers often lack support in providing preferences and insights in exploring the solutions available.

We explore the combination of learnable evolutionary models with interactive indicator-based evolutionary multiobjective optimization to create a learnable evolutionary multiobjective optimization method. Furthermore, we leverage interpretable machine learning to provide decision makers with potential insights about the problem being solved in the form of rule-based explanations. In fact, we show that a learnable evolutionary multiobjective optimization method can offer advantages in the search for solutions to a multiobjective optimization problem. We also provide an open source software framework for other researchers to implement and explore our ideas in their own works.

Our work is a step towards establishing a new paradigm in the field on multiobjective optimization: explainable and learnable multiobjective optimization. We take the first steps towards this new research direction and provide other researchers and practitioners with necessary tools and ideas to further contribute to this field.


Keywords: multi-objective optimisation; artificial intelligence; machine learning; modelling (representation); application frameworks

Free keywords: multiobjective optimization; evolutionary multiobjective optimization; learnable evolutionary models; explainable artificial intelligence


Contributing organizations


Ministry reporting: Yes

VIRTA submission year: 2023

JUFO rating: 1


Last updated on 2024-12-10 at 19:15