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Dominance in Incomplete Solvers for the Multiple Knapsack Problem

We investigate incomplete search algorithms in a search space constrained by a dominance criterion. We propose a new genetic algorithm for the Multiple Knapsack Problem (MKP) which searches a space of candidates which are not dominated according to a dominance criterion. We compare the new algorithm to previous heuristics for the MKP, as well as alternative algorithms, and show experimentally that our new algorithm yields the best performance on difficult instances where item weights and profits are highly correlated.