Análise comparativa entre novos operadores genéticos incluídos no Framework Evolutionary Algorithms - BARRETO, Adriano Silva (2019)
New operators for Genetic Algorithms are being proposed daily by the academic community to improve the performance of this technique. It is necessary to know their performance in order to make good use of these improvements and to know their limitations and strengths. This work is a comparative analysis of variants of genetic algorithms that were created and implemented by the academic community. The purpose of this study is to perform comparisons between variants of genetic operators to identify the existing differences in performance offered by them. The genetic operators that were researched in this work are: the transgenic operator, the operator of parasite diversity and the adaptive immune operator based on information entropy. These operators were implemented and evaluated through tests with multimodal functions. An analysis was made among the genetic algorithms in order to evaluate if the algorithm finds the solution and the convergence guarantee. Some metrics that were evaluated in the operators were the robustness to optimize the function with a given error tolerance and a convergence analysis. It was considered in this work that the solution is found according to various defined precisions, where the error is less than or equal to 10−3, 10−2 and 10−1. After the tests, the performance analysis performed among the implemented operators showed that all the operators obtained good results for the functions with a good convergence and the operator that obtained the best results was the adaptive immune operator.