Best network flow bounds for the quadratic knapsack problem
COMO '86 Lectures given at the third session of the Centro Internazionale Matematico Estivo (C.I.M.E.) on Combinatorial optimization
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
Exact Solution of the Quadratic Knapsack Problem
INFORMS Journal on Computing
Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The quadratic multiple knapsack problem and three heuristic approaches to it
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Optimization of performance of genetic algorithm for 0-1 knapsack problems using taguchi method
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
A swarm intelligence approach to the quadratic multiple knapsack problem
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A memetic algorithm for the quadratic multiple container packing problem
Applied Intelligence
Generalized quadratic multiple knapsack problem and two solution approaches
Computers and Operations Research
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The Quadratic Multiple Knapsack Problem (QMKP) is a generalization of the quadratic knapsack problem, which is one of the well-known combinatorial optimization problems, from a single knapsack to k knapsacks with (possibly) different capacities. The objective is to assign each item to at most one of the knapsacks such that none of the capacity constraints are violated and the total profit of the items put into the knapsacks is maximized. In this paper, a genetic algorithm is proposed to solve QMKP. Specialized crossover operator is developed to maintain the feasibility of the chromosomes and two distinct mutation operators with different improvement techniques from the non-evolutionary heuristic are presented. The performance of the developed GA is evaluated and the obtained results are compared to the previous study in the literature.