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Found 4 papers, 3 papers with code
Self-Improved Learning for Scalable Neural Combinatorial Optimization
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.
An Example of Evolutionary Computation + Large Language Model Beating Human: Design of Efficient Guided Local Search
Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design.
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts.
Dynamic Multi-objective Ensemble of Acquisition Functions in Batch Bayesian Optimization
To select batch candidate solutions, we rank these non-dominated solutions into several layers according to their relative performance on the three acquisition functions.
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