In this chapter, we describe the Construct, Merge, Solve & Adapt (CMSA) algorithm, a metaheuristic framework designed to address hard combinatorial optimization problems. CMSA combines the probabilistic construction of solutions within an adaptive, iterative process with solution merging and exact optimization techniques. The algorithm probabilistically constructs solutions through heuristic methods, which are then merged into a reduced subproblem. This subproblem is solved using exact optimization approaches, mostly integer programming. Based on the feedback from this solving phase, the algorithm adapts its construction and merging strategies in subsequent iterations, progressively finding solutions of improving quality over time. CMSA’s hybrid nature allows it to balance between the speed of heuristic construction and the accuracy of exact methods, making it particularly effective for large-scale problems. After a general description of standard CMSA, we outline a recent self-adaptive variant and a variant that uses reinforcement learning to improve the search process. All three algorithm variants are applied to the classical maximum independent set problem. Moreover, an experimental evaluation is provided to show their comparative behavior.