摘要：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.
