Research on a New DNA-GA Algorithm Based on P System

  • Shuguo Zhao
  • Xiyu Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


In recent years, DNA-GA algorithms, which attracts many scholars’ attention, combine the DNA encoding method with Genetic algorithm. It effectively overcomes GA’s limitation such as premature convergence, poor local search capability and binary Hamming cliffs problems. In this work, a new DNA-GA algorithm based on P system (PDNA-GA) is proposed to improve the performance of DNA-GA algorithms by combining the parallelism of P system in Membrane Computing. The performance of PDNA-GA in typical benchmark functions is studied. The experimental results demonstrate that the proposed algorithm can effectively yield the global optimum with high efficiency.


Membrane computing P system DNA-GA Fitness Genetic operators 



This work is supported by National Science Fund of China (No. 61170038), Science Fund of Shandong province (No. ZR2011FM001) and Social Science Fund of Shandong province (No. 11CGLJ22).


  1. 1.
    Huang L (2007) Research on membrane computing optimization methods. Zhejiang University, ChinaGoogle Scholar
  2. 2.
    Ding YS, Ren LH, Shao SH (2002) DNA computing and soft computing. Science press, ChinaGoogle Scholar
  3. 3.
    Tao JL, Wang N (2007) DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes. Comput Chem Eng 31(12):1602–1618Google Scholar
  4. 4.
    Chen X, Wang N (2010) Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm. Chem Eng Process 49(10):1076–1083Google Scholar
  5. 5.
    Zhang L, Wang N (2013) A modified DNA genetic algorithm for parameter estimation of the 2-Chlorophenoloxidation in supercritical water. Appl Math Model 37(3):1137–1146Google Scholar
  6. 6.
    Wang K, Wang N (2011) A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil. Chem Eng J 167(1):228–239Google Scholar
  7. 7.
    Dai K, Wang N (2012) A hybrid DNA based genetic algorithm for parameter estimation of dynamic systems. Chem Eng Res Des 90(12):2235–2246CrossRefMathSciNetGoogle Scholar
  8. 8.
    Paun G (2000) Computing with membranes. J Comput Syst Sci 61(1):108–143Google Scholar
  9. 9.
    Escuela G, Gutierrez-Naranjo MA (2010) An application of genetic algorithms to membrane computing. In: Proceedings of eighth brainstorming week on membrane computing, pp 101–108Google Scholar
  10. 10.
    Liu XX, Zhang GL (2010) A research on population size impaction on the performance of genetic algorithm. North China Electric Power University, ChinaGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Management Science and EngineeringShandong Normal UniversityJinanChina

Personalised recommendations