Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning

Aickelin, Uwe; Khorshidi, Hadi Akbarzadeh; Qu, Rong; Charkhgard, Hadi

Abstract

We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in machine learning. Every machine-learning technique has hyperparameters that can be tuned using evolutionary computation and optimization, considering normally multiple criteria, such as bias, variance, complexity, and fairness in model selection. Multiobjective evolutionary optimization can help meet these criteria for optimizing machine-learning models. Some of the existing approaches address these multiple criteria by transforming the problem into a single-objective optimization problem. However, multiobjective optimization models are able to outperform single-objective ones in contributing to multiple intended objectives (criteria). In recent years, evolutionary computation has been shown to be the premier method for solving multiobjective optimization problems (MOPs), producing both optimal and diverse solutions beyond the capabilities of other heuristics. This is particularly true for very large solution spaces, which is the case in real-world machine-learning problems with many features.

Citation

Aickelin, U., Khorshidi, H. A., Qu, R., & Charkhgard, H. (2023). Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning. IEEE Transactions on Evolutionary Computation, 27(4), 746-748. https://doi.org/10.1109/tevc.2023.3292528

• Journal Article Type: Editorial
• Acceptance Date: May 26, 2023
• Online Publication Date: Aug 1, 2023
• Publication Date: 2023-08
• Deposit Date: Aug 8, 2023
• Journal: IEEE Transactions on Evolutionary Computation
• Print ISSN: 1089-778X
• Electronic ISSN: 1941-0026
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Not Peer Reviewed
• Volume: 27
• Issue: 4
• Pages: 746-748
• DOI: https://doi.org/10.1109/tevc.2023.3292528
• Keywords: Computational Theory and Mathematics; Theoretical Computer Science; Software
• Public URL: https://nottingham-repository.worktribe.com/output/23864936
• Publisher URL: https://ieeexplore.ieee.org/document/10198500