Cell-Free Massive MIMO is a highly promising approach to enhance network capacity by moving a large number of distributed access points (AP) closer to mobile users while utilizing simple matched filtering and conjugate beamforming. Recent work using minimum mean-squared-error (MMSE) receiver that suppress multi-user interference (MUI) shows significant capacity increase, but at the cost of high computational complexity and residual MUI enhancement. We propose a significantly lower complexity adaptive approach where central processing unit (CPU) removes MUI without amplifying the residual interference. It does so dynamically by using available knowledge of channel estimates to perform joint process of combining selected strongest AP signals for each user and subtracting the sum of interference estimates from other users at the same time. We provide signalto-interference plus noise-ratio (SINR) and complexity analyses backed by numerical results to show the superiority of this approach compared with the state-of-the-art techniques.