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 …

In the industrial and manufacturing field, many problems require the tuning of the
parameters of complex empirical or theoretical models by means of the exploitation of 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 the use
of these latter approaches is the risk of converging to a sub-optimal solution due to
characteristics of the search surface which is determined both by the model and the data …