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

doi:10.1007/s12204-012-1251-7

Abstract

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:

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:

CLC Number:

TP 301.6

TAO Yan-yun1 (陶砚蕴), CAO Jian 1,2 (曹健), LI Ming-lu 1,2 (李明禄). Genetic Programming Using Dynamic Population Variation for Computational Efforts Reduction in System Modeling[J]. Journal of shanghai Jiaotong University (Science), 2012, 17(2): 190-196.

References

[1] Grosman B, Lewin D R. Adaptive genetic programming for steady-state process modeling [J]. Computers and Chemical Engineering, 2004, 28(12): 2779-2790.

[2] Koza J R. Genetic programming: On the programming of computers by means of natural selection [M].Cambridge: MIT Press, 1992.

[3] Miller J F, Job D, Vassilev V K. Principles in the evolutionary design of digital circuits. Part I [J]. Genetic Programming and Evolvable Machines, 2000,

1: 7-35.

[4] Miller J F, Job D, Vassilev V K. Principles in the evolutionary design of digital circuits. Part II [J].Genetic Programming and Evolvable Machines, 2000,

1: 259-288.

[5] Miller J F, Thomson P. Cartesian genetic programming [C]//Proceedings of the 3rd European Conference on Genetic Programming. Edinburgh, UK: Springer-

Verlag, 2000: 121-132.

[6] Sykulski J K. Reducing computational effort in field optimization problems [J]. The International Journal for Computation and Mathematics in Electrical and

Electronic Engineering, 2004, 23(1): 159-172.

[7] Lee K J, Zhang B T. Learning robot behaviors by evolving genetic programs [C]//Proceedings of the 26th Annual Confjerence of the IEEE Industrial Electronics

Society. Nagoya, Japan: IEEE, 2000: 2867-2872.

[8] Niehaus J, Banzhaf W. More on computational effort statistics for genetic programming [C]//Proceedings of the 6th European Conference on

Genetic Programming. Berlin: Springer-Verlag, 2003:713-783.

[9] Walker M, Edwards H, Messom C. Confidence intervals for computational effort comparisons [C]//Proceedings of the 10th European Conference on

Genetic Programming. Valencia, Spain: Springer-Verlag, 2007: 23-32.

[10] Shi X H, Liang Y C, Lee H P, et al. An improved GA and a novel PSO-GA-based hybrid algorithm [J].Information Processing Letters, 2005, 93(5): 255-261.

[11] Fern´andez F, Tomassini M, Vanneschi L. Saving computational effort in genetic programming by means of plagues [C]//Proceedings of the 2003 Congress

on Evolutionary Computation. Canberra, Australia:IEEE, 2003: 2042-2049.

[12] Kouchakpour P, Zaknich A, Br¨aunl T. Population variation in genetic programming [J]. Information Sciences, 2007, 177(17): 3438-3452.

[13] Tomassini M, Vanneschi L, Cuendet J, et al. A new technique for dynamic size populations in genetic programming [C]//Proceedings of the 2004 IEEE

Congress on Evolutionary Computation. Oregon, Portland:IEEE, 2004: 486-493.

[14] Kouchakpour P, Zaknich A, Br¨aunl T. Dynamic population variation in genetic programming [J]. Information Sciences, 2009, 179(08): 1078-1091.