Evolutionary Algorithms (EAs) are a set of optimization techniques that have become incredibly popular in the last decades. As they are general purpose algorithms, they have been applied to a wide range of problems, many of them from industrial or scientific disciplines. Several approaches have been proposed, each of them implementing the biological metaphor in their own particular way. This provides each of these evolutionary approaches with different search characteristics, which make them more suitable to different types of problems. This diversity of Evolutionary Algorithms makes possible to face a wider range of optimization problems. However, the selection of a particular Evolutionary Algorithm becomes a crucial decision that can determine the quality of the obtained results. Furthermore, some studies show that synergies among different Evolutionary Algorithms are possible when they are combined appropriately. The use of hybrid algorithms to deal with specific and complex real-world problems is also a fact that proves that hybridization is a powerful tool far beyond the individual algorithms. In this work, the combination of different evolutionary approaches is analyzed thanks to a framework that provides a robust and complete support for the development of Hybrid EAs. This framework is called Multiple Offspring Sampling (MOS) and is based on the key concept of a reproductive technique, which offers an abstraction of the mechanisms used by the different evolutionary approaches to create new individuals, i.e., the particular operators, parameters and encodings of the solutions present in the canonical versions of these algorithms. However, it is now the MOS framework, and not the individual algorithms, the one responsible for creating new individuals by means of the available reproductive techniques. The hybrid algorithms developed with the MOS framework can dynamically evaluate the performance of the different reproductive techniques and adjust their participation accordingly. Several strategies have been proposed for the evaluation of the quality of the techniques and the adjustment of their participation, including some of the more classic alternatives present in the literature to assess the convenience of using the mechanisms offered by MOS. Additionally, the automatic learning of these hybridization strategies by means of Reinforcement Learning mechanisms has also been studied. To conclude, the proposed framework has been tested on a set of well-known problems, from both discrete and continuous domains, obtaining statistically meaningful results confirming that an appropriate combination of different search strategies can lead to an outstading performance compared to the individual algorithms.