This paper presents the RB-IPOP-CMA-ES algorithm which is an enhanced version of IPOP-CMA-ES. The algorithm uses midpoint of the population as an approximation of the optimum. The midpoint fitness is also used to introduce a new restart trigger for IPOP. Other IPOP restart triggers and parameters are also corrected. The performance of the proposed approach is evaluated on 30 problems from the CEC 2017 benchmark for 10, 30, 50 and 100 dimensions. The results confirm that RB-IPOP-CMA-ES achieves better results than its version that does not utilize midpoint and is a considerable improvement over a plain IPOP-CMA-ES.