Features for the 0-1 knapsack problem based on inclusionwise maximal solutions

1 code implementation • 16 Nov 2022 • Jorik Jooken, Pieter Leyman, Patrick De Causmaecker

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.

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

2 code implementations • 22 Oct 2020 • Jorik Jooken, Pieter Leyman, Tony Wauters, Patrick De Causmaecker

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.

Combinatorial Optimization Scheduling

Evolving test instances of the Hamiltonian completion problem

1 code implementation • 5 Oct 2020 • Thibault Lechien, Jorik Jooken, Patrick De Causmaecker

Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons.