A hybrid evolutionary computation approach with its application for optimizing text document clustering

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Knowledge discovery and management,Evolutionary computation,Particle swarm optimization,Quantum-behaved particle swarm optimization

论文评审过程：Available online 8 November 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.11.003