This paper investigates integrating evolutionary multitasking with Edge Assembly Crossover (MT-EAX) to solve the classical Travelling Salesperson Problem and demonstrates that the advantage of MT-EAX derives from increased diversity through parallel search in early generations, which can be successfully preserved using...
L. Wigney, Aneta Neumann, Y. Ong et al.· Parallel Problem Solving fro...· 0 citations
This paper model MTO as a sequence of sequential transfer optimization problems, concentrating evaluations on a single target per iteration, and proposes a likelihood-informed task prioritization mechanism to maximize transfer utility by identifying the task most likely ready for knowledge integration.
Tingyang Wei, Hao-Feng Wu, Ananda Phan Iman et al.· 0 citations
A multi-objective neural basis model (MONBM) framework based on multi-objective evolutionary learning to consider accuracy, interpretability, and fairness simultaneously is proposed and demonstrates how multi-objective optimization can be combined with self-interpretable models to reveal relationships among trustworthi...
Ziming Wang, Changwu Huang, Ke Tang et al.· Neural Networks· 1 citation
This work forms the specialization of pretrained virtual screening models to individual pockets as a test-time adaptation problem and proposes PETA, a parameter-efficient framework that directly adapts pretrained model at test time and outperforms both pretrained and fully retrained baselines while updating only the La...
Jia-Qi Lin, Ying-Hua Yao, Chang-Ran Wang et al.· 0 citations
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