A hybrid evolutionary computation approach with its application for optimizing text document clustering
We propose a novel hybrid evolutionary computation approach for optimizing text clustering. GA improves the initializing strategy of QPSO and yields a preliminary optimization. A new position update approach is proposed to normalize the search space of particles. This approach enhances the performance evaluated by both fitness and F-measure.
evolutionary computation knowledge discovery and management particle swarm optimization quantum-behaved particle swarm optimization