Kjartan Pétursson - Academia.edu (original) (raw)
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German Research Center for Artificial Intelligence
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Papers by Kjartan Pétursson
This thesis introduces two novel search methods for automated design and discovery of heuristic b... more This thesis introduces two novel search methods for automated design and discovery of heuristic branching rules, employed by the branch-and-bound algorithm for Mixed Integer Programming (MIP). For this experiment, SCIP, an open source MIP solver is used. The rules are specifically designed/trained to be effective at solving the 0-1 Multidimensional Knapsack Problem. A supervised approach is developed, utilizing data collected at the root node of the solution tree, where each instance is solved once for each branching candidate available at the root. Rules are then designed via feature selection with weights inferred through evolutionary search. The resulting branching rules are designed for use at the root node only where the most important branching decisions are made. Another, nonsupervised method, assigns weights to heuristic components selected beforehand. Weights are assigned using (1+1)CMA-ES which evaluates performance by solving every training instance with SCIP. For both me...
This thesis introduces two novel search methods for automated design and discovery of heuristic b... more This thesis introduces two novel search methods for automated design and discovery of heuristic branching rules, employed by the branch-and-bound algorithm for Mixed Integer Programming (MIP). For this experiment, SCIP, an open source MIP solver is used. The rules are specifically designed/trained to be effective at solving the 0-1 Multidimensional Knapsack Problem. A supervised approach is developed, utilizing data collected at the root node of the solution tree, where each instance is solved once for each branching candidate available at the root. Rules are then designed via feature selection with weights inferred through evolutionary search. The resulting branching rules are designed for use at the root node only where the most important branching decisions are made. Another, nonsupervised method, assigns weights to heuristic components selected beforehand. Weights are assigned using (1+1)CMA-ES which evaluates performance by solving every training instance with SCIP. For both me...