Hybridizing Genetic Algorithm with Cross Entropy for Solving Continuous Functions

In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions with different characteristics, using different configurations.