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Found 3 papers, 3 papers with code
Features for the 0-1 knapsack problem based on inclusionwise maximal solutions
We show that the proposed features contain important information related to the empirical hardness of a problem instance that was missing in earlier features from the literature by training machine learning models that can accurately predict the empirical hardness of a wide variety of 0-1 knapsack problem instances.
NICE: An Algorithm for Nearest Instance Counterfactual Explanations
We propose four versions of NICE, one without optimization and, three which optimize the explanations for one of the following properties: sparsity, proximity or plausibility.
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems
The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees and represents the state-of-the-art algorithm for a number of games.
