Differential Evolution Based on Learnable Evolution Model for Function Optimization

With the advance of technology, the generation of massive amounts of information grows every day, generating complex problems difficult to manage in an efficient way. Therefore, researchers have studied and modeled the way in which natural biological systems react and behave in certain situations, allowing to developed algorithms that exhibit a capacity to learn and/or adapt to new situations, obtaining better results than traditional approaches. In this article we present a new variant of the Differential Evolution (DE) algorithm inspired by the concept of the Learnable Evolution Model (LEM) to enhance the search capability through a selection mechanism based on machine learning to create a set of rules that allows the inferring of new candidates in the population that emerge not only the random scan. The proposed algorithm is tested and validated on a set of 23 bechmark test functions and its performance is compared with other metaheuristics. Results indicate that the proposed DE+LEM is competitive with other metaheuristic.

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