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Found 4 papers, 1 papers with code
A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms
We employ KANs for regression and classification tasks, focusing on the selection of promising solutions during the search process, which consequently reduces the number of expensive function evaluations.
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm, namely CDM-PSL, for expensive MOBO.
Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
To address these limitations, we design a Geometry-Aware Pareto set Learning algorithm named GAPL, which provides a novel geometric perspective for neural MOCO via a Pareto attention model based on hypervolume expectation maximization.
Pointer Networks Trained Better via Evolutionary Algorithms
With this advantage, this paper is able to at the first time report the results of solving 1000-dimensional TSPs by training a PtrNet on the same dimensionality, which strongly suggests that scaling up the training instances is in need to improve the performance of PtrNet on solving higher-dimensional COPs.
