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Found 8 papers, 6 papers with code
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
To address these challenges, we formalize model merging as a multi-objective optimization problem and propose an automated optimization approach named MM-MO.
Large Language Models as Surrogate Models in Evolutionary Algorithms: A Preliminary Study
Specifically, we formulate model-assisted selection as a classification and regression problem, utilizing LLMs to directly evaluate the quality of new solutions based on historical data.
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.
Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings.
Evolutionary Retrosynthetic Route Planning
The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53. 9%.
Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.
Self-supervised Learning and Graph Classification under Heterophily
Self-supervised learning has shown its promising capability in graph representation learning in recent work.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
