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—Feature selection (FS) is a fundamental pattern recognition problem, which aims to reduce the number of features used for recognition, with an acceptable accuracy. Unfortunately, the FS problem belongs to the set of NP-hard problems, that many approaches have been developed to solve it. In this paper a metaheuristic based on artificial bee colony(More)
Traveling salesman problem (TSP) is a popular routing problem, which is a sub-problem of many application domains such as transportation, network communication, vehicle routing and integrated circuits designs. Among many approaches which have been proposed for TSP so far, evolutionary algorithms effectively applied to solve it, and attempt to avoid trapping(More)
Knapsack Problem (KP) is a most popular subset selection problem. The aim is to assign an optimal subset among all original items to a knapsack, such that the overall profit of the selected items be maximized, while the total weight of them does not exceed the capacity of the knapsack. Artificial Bee Colony (ABC) algorithm is a new metaheuristic with a(More)
—Travelling Salesman Problem (TSP) belongs to the class of NP-Complete problems. It has been proved that evolutionary algorithms are effective and efficient, with respect to the traditional methods for solving NP-Complete problems like TSP, with avoidance trapping in local minima areas. Artificial Bee Colony (ABC) is a new swarm-based optimization(More)
Multiple Knapsack Problem (MKP) is a most popular multiple subset selection problem that belongs to the class of NP-Complete problems. The aim is to assign optimal subsets among all original items to some knapsacks, such that the overall profit of all selected items be maximised, while the total weight of all assigned items to any knapsack does not exceed(More)
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