Mahmoud Zennaki

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In this paper, we approximately solve the multiple-choice multi-dimensional knapsack problem. We propose a hybrid algorithm based on branch and bound method and Pareto-algebraic operations. The algorithm starts by an initial solution and then combines one-by-one groups of the problem instance to generate partial solutions in each iteration. Most of these(More)
Le présent article rentre dans le cadre d'un projet ambitieux qui a pour objectif l'intégration des techniques d'apprentissage aux métaheuristiques pour contribuer à la résolution des problèmes d'optimisation combinatoire NP-difficiles. L'approche proposée consiste à incorporer un apprentissage non supervisé aux métaheuristiques à population de solutions(More)
We investigate the possibility of using kernel clustering and data fusion techniques for solving hard combinatorial optimization problems. The proposed general paradigm aims at incorporating unsupervised kernel methods into population-based heuristics, which rely on spatial fusion of solutions, in order to learn the solution clusters from the search(More)
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