An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper.Our approach introduces an extension of Mendonca-Cipolla and G.Chesis self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix.Specifically,a suitable dynamic online cost function is generated according to the property of the three singular values.The visual servo process is carried out simultaneous to the dynamic self-calibration,and then the cost function is minirmzed using the adaptive genetic algorithm instead of the gradient descent method in G.Chesis approach.Moreover,this method overcomes the limitation that the initial parameters must be selected close to the true value,which is not constant in many cases.It is not necessary to know exactly the camera intrinsic parameters when using our approach,instead,coarse coding bounds ot the five parameters are enough for the algorithm,which can be done once and for all off-line.Besides,this algorithm does not require knowledge of the 3D model of the object.Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbalaons of camera parameters,and it is an effective and efficient visual scrvo algorithm.