In the industrial and manufacturing fields, many problems require tuning of the parameters of complex models by means of exploitation of empirical data. In some cases, the use of analytical methods for the determination of such parameters is not applicable; thus, heuristic methods are employed. One of the main disadvantages of these approaches is the risk of converging to “suboptimal” solutions. In this article, the use of a novel type of genetic algorithm is proposed to overcome this drawback. This approach exploits a fuzzy inference system that controls the search strategies of genetic algorithm on the basis of the real-time status of the optimization process. In this article, this method is tested on classical optimization problems and on three industrial applications that put into evidence the improvement of the capability of avoiding the local minima and the acceleration of the search process.