Algoritmo genético com regressão: busca direcionada através de aprendizado de máquina

Abstract

Optimization problems are common in many areas. In engineering, in many situations optimization problems were modeled disregarding certain characteristics of the studied phenomenon in order to simplify the simulations during the search process. However, over time, the evolution of the machines allowed the modeling of optimization problems with more information, approaching the models in the most reliable way possible. In this way, a significant portion of these problems requires a high computational cost to perform the evaluations of candidate solutions, making many of them difficult to analyze and simulate. Thus, the objective of this work is the use of machine learning methods coupled with an optimization algorithm with the purpose of directing the search process of a genetic algorithm, inserting new good quality solution into the population at each generation with the intention of reducing the high computational cost of finding the optimal solutions. In addition, a comparative study is carried out to verify which machine learning methods obtain good results in the proposed technique. The experiments are performed on optimization problems with a high computational cost commonly found in the literature.