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

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2 Author(s)
D. Duzanec ; Ziegler d.o.o., Construction and Development Department, Zagreb, Croatia ; Z. Kovacic

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 (muGA) by enforcing gradual contraction of the space of candidate solutions during optimization. The enhanced muGA 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.

Published in:

Industrial Technology, 2009. ICIT 2009. IEEE International Conference on

Date of Conference:

10-13 Feb. 2009