International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2569

Full Length Research Paper

The experimental parameters optimization approach using a learning genetic algorithm

Lu Lu* and Xiuxia Quan
School of Computer Science and Engineering, South China Technology, Guangzhou 510006, P. R. China.
Email: [email protected]

  •  Accepted: 24 March 2011
  •  Published: 04 April 2011



A learning genetic algorithm is proposed to solve the experimental parameters optimization problem. This method can not only enhance the efficiency of genetic algorithm through the pre-given user experience, but also improve the efficiency of genetic algorithm via learning the knowledge obtained from the optimization process. Experimental results suggest that the learning genetic algorithm can effectively optimize the experimental parameters.


Key words: Genetic algorithms, experimental parameters optimization, combinatorial optimization.