ACM Trans. Evol. Learn. Optim.

Keyphrases

Publications

volume 4, number 1, 2024

Tiwonge Msulira Banda, Alexandru-Ciprian Zavoianu, Andrei Petrovski, Daniel Wöckinger, Gerd Bramerdorfer
A Multi-Objective Evolutionary Approach to Discover Explainability Tradeoffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behaviour of Electrical Machines. ACM Trans. Evol. Learn. Optim. 4 (1) (2024)
Giovanni Misitano
Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method. ACM Trans. Evol. Learn. Optim. 4 (1) (2024)
Giorgia Nadizar, Luigi Rovito, Andrea De Lorenzo, Eric Medvet, Marco Virgolin
An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming. ACM Trans. Evol. Learn. Optim. 4 (1) (2024)
Ziming Wang, Changwu Huang, Yun Li, Xin Yao
Multi-objective Feature Attribution Explanation For Explainable Machine Learning. ACM Trans. Evol. Learn. Optim. 4 (1) (2024)
Jaume Bacardit, Alexander E. I. Brownlee, Stefano Cagnoni, Giovanni Iacca, John A. W. McCall, David J. Walker
Introduction to the Special Issue on Explainable AI in Evolutionary Computation. ACM Trans. Evol. Learn. Optim. 4 (1) (2024)

volume 4, number 2, 2024

volume 4, number 3, 2024

volume 3, number 1, 2023

volume 3, number 2, 2023

volume 3, number 3, 2023

volume 3, number 4, 2023

volume 2, number 1, 2022

volume 2, number 2, 2022

volume 2, number 3, 2022

volume 2, number 4, 2022

volume 1, number 4, 2021