Performance analysis-based GA parameter selection and increase of µGA accuracy by gradual contraction of solution space

  • Authors:

  • D. Duzanec;Z. Kovacic

  • Affiliations:

  • Ziegler d.o.o., Construction and Development Department, Zagreb, Croatia;University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Control and Computing Engineering, Zagreb, Croatia

  • Venue:
  • ICIT '09 Proceedings of the 2009 IEEE International Conference on Industrial Technology
  • Year:
  • 2009

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Abstract

Although methods for design of genetic algorithms (GA) are well established, general expressions for determination of optimal GA parameters are still missing. There is also a problem of possible inaccuracy of a found solution. This paper describes a GA performance analysis for a selected vector-based optimization problem that has led to useful GA parameter selection criteria. The paper also describes a new method for increasing the precision of a complementary micro genetic algorithm (μGA) by enforcing gradual contraction of the space of candidate solutions during optimization. The enhanced μGA has been tested on the model of a 13-DOF tentacle robot, and the performance analysis showed significant improvement of accuracy without affecting the duration of the algorithm.