Per-Instance Algorithm Selection and Automatic Algorithm Configuration have recently gained important interests. However, these approaches face many limitations. For instance, the performance of these methods is deeply influenced by factors like the accuracy of the underlying prediction model, features space correlation, incomplete performance space for new instances, instances sampling and many others. In this paper, an effort to address such limitations is described. Indeed, we propose a cooperative architecture, labeled as the "SAPIAS" concept, composed of a self-adaptive online Algorithm Selection system and an offline Automatic Algorithm Configuration system, working together in order to deliver the most accurate performance. Additionally, SAPIAS is proposed as a methodic concept that the metaheuristics community might adopt to fill in the gap between theory and practice in the field, by providing for theoreticians the ability to continuously analyze the evolution of the problems characteristics and the behavior of the solving techniques as well as providing a ready to use solving framework for practitioners.