SOME FURTHER EXPERIMENTS WITH THE GENETIC ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM
In this paper, some further experiments with the genetic algorithm (GA) for the quadratic assignment problem (QAP) are described. We propose to use a particle-swarm-optimization-based approach for tuning the values of the parameters of the genetic algorithm for solving the QAP. The resulting combined self-adaptive swarm optimization- genetic algorithm enables to efficiently auto-configure the control parameters for GA — which leads to excellent quality solutions, especially for the real-life like (structured) QAP instances.
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