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The study conducted in this work analyses the interactions between different Evolutionary Algorithms when they are hybridized. For this purpose, the phylogenetic tree of the best solution reported by the hybrid algorithm is reconstructed, and the relationships among the ancestors of this solution are established. For each of these ancestors, the evolutionary techniques that generated that solution and the fitness increment introduced compared to its parents are recorded. The study reveals a structured interaction among the different evolutionary techniques that makes the hybrid algorithm to outperform each of its composing algorithms when executed individually. The Multiple Offspring Sampling framework has been used to develop the Hybrid EA studied in this work and the experiments have been conducted on the well-known CEC 2005 Benchmark for continuous optimization.