Error Evaluation of Planar Curve Profile Based on an Improved Genetic Algorithm

To improve the measuring accuracy of planar curve profile error, an improved genetic algorithm is put forward to realize self-adaptive matching of measured curve, eliminating the position deviation during error evaluation of planar curve profile. It not only improves the efficiency and precision of the algorithm but also prevents premature convergence to local optimal solutions by introducing a relative fitness function, setting the dynamic mutation probability, and re-inserting. An instance of planar curve profile error evaluation is used to compare the improved genetic algorithm with the traditional genetic algorithm. The example of single running result shows that the improved genetic algorithm runs faster than the traditional one and its precision increases by 29.8%. The repeated running results also show a faster convergent speed of improved genetic algorithm and its precision increases by 39.1%, compared with the traditional one. After the self-adaptive matching of the measured curve by improved genetic algorithm, the planar curve profile error decreases by 52.4%, compared with the value without the curve matching.