上海交通大学学报（英文版） ›› 2012, Vol. 17 ›› Issue (2): 190-196.doi: 10.1007/s12204-012-1251-7

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Genetic Programming Using Dynamic Population Variation for Computational Efforts Reduction in System Modeling

TAO Yan-yun1 (陶砚蕴), CAO Jian 1,2∗ (曹健), LI Ming-lu 1,2 (李明禄)   

  1. (1. Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. Shanghai Key Laboratory of Scalable Computing and System, Shanghai 200240, China)
  • 出版日期:2012-04-28 发布日期:2012-05-31

Genetic Programming Using Dynamic Population Variation for Computational Efforts Reduction in System Modeling

TAO Yan-yun1 (陶砚蕴), CAO Jian 1,2∗ (曹健), LI Ming-lu 1,2 (李明禄)   

  1. (1. Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. Shanghai Key Laboratory of Scalable Computing and System, Shanghai 200240, China)
  • Online:2012-04-28 Published:2012-05-31

摘要： In this paper, we propose genetic programming (GP) using dynamic population variation (DPV) with four innovations for reducing computational efforts. A new stagnation phase definition and characteristic measure are defined for our DPV. The exponential pivot function is proposed to our DPV method in conjunction with the new stagnation phase definition. An appropriate population variation formula is suggested to accelerate convergence. The efficacy of these innovations in our DPV is examined using six benchmark problems. Comparison among the different characteristic measures has been conducted for regression problems and the new proposed measure outperformed other measures. It is proved that our DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed DPV methods and standard genetic programming in most cases. Meanwhile, our DPV approach introduced in GP could also rapidly find an excellent solution as well as standard GP in system modeling problems.

关键词: dynamic population variation (DPV), stagnation phase, exponential pivot function, computational effort, average number of evaluation, diversity

Abstract: In this paper, we propose genetic programming (GP) using dynamic population variation (DPV) with four innovations for reducing computational efforts. A new stagnation phase definition and characteristic measure are defined for our DPV. The exponential pivot function is proposed to our DPV method in conjunction with the new stagnation phase definition. An appropriate population variation formula is suggested to accelerate convergence. The efficacy of these innovations in our DPV is examined using six benchmark problems. Comparison among the different characteristic measures has been conducted for regression problems and the new proposed measure outperformed other measures. It is proved that our DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed DPV methods and standard genetic programming in most cases. Meanwhile, our DPV approach introduced in GP could also rapidly find an excellent solution as well as standard GP in system modeling problems.

Key words: dynamic population variation (DPV), stagnation phase, exponential pivot function, computational effort, average number of evaluation, diversity

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