This paper aims to predict optimal solutions for combinatorial optimization problems (COPs) via machine learning (ML). To find high-quality efficiently, existing work uses a ML prediction of the solution guide heuristic search, where model is trained offline under supervision solved problem instances with known solutions. sufficient accuracy, it critical provide adequate features that can effec...

This paper presents improved Ant Colony Optimization (ACO) algorithms for data mining. The goal of the algorithms is to extract classification rules from data. The traditional Ant Colony Optimization algorithm is enhanced with genetic operators to develop improved ACO algorithms. The genetic operators like crossover, mutation are used to develop Ant Colony Optimization with Crossover (ACOC), AC...

We give an alternative proof to a characterization theorem of Gurvich for Boolean functions whose formula size is exactly the number of variables. Those functions are called Read-Once. We use methods of combinatorial optimization and give as a corollary an alternative proof for some results of Seymour.

Production-inventory games were introduced in [Guardiola, L.A., Meca, A., Puerto, J. (2008). Production-Inventory games: A new class of totally balanced combinatorial optimization games. Games Econom. Behav. doi:10.1016/j.geb.2007.02.003] as a new class of totally balanced combinatorial optimization games. From among all core-allocations, the Owen point was proposed as a specifically appealing ...