An orthogonal genetic algorithm for multimedia multicast routing
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
A Chaotic Neural Network Combined Heuristic Strategy for Multidimensional Knapsack Problem
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Solving multidimensional 0---1 knapsack problem with an artificial fish swarm algorithm
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems
Journal of Global Optimization
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In this paper, a genetic algorithm based on the orthogonal design for solving the multidimensional knapsack problems is proposed. The orthogonal design with the factor analysis, an experimental design method, is applied to the genetic algorithm, to make the algorithm be more robust, statistically sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a small, but representative sample of points as offspring. After all of the better genes of these offspring are selected, an optimal offspring better than its parents is then generated in the end. Moreover, a check-and-repair operator is adopted to make the infeasible chromosomes generated by the crossover and mutation operators feasible, and make the feasible chromosomes better. The simulation results show that the proposed algorithm can find optimal or close-to-optimal solutions with less computation burden.