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

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.