Multi-Objective Evolutionary Algorithm for Identifying the Important Parameters of a Complex System

In recent decades, considerable research efforts have been devoted to using machine learning or data mining techniques to automatically discover the parameters in multi-objective functions. Among these techniques, the genetic algorithms have been recognized to be particularly powerful in multi-objective problems. Genetic algorithms play a significant role, as search techniques for handling complex spaces in many fields. These algorithms are based on the underlying genetic process and are optimization algorithms based on the mechanics of natural genetics and natural selection. Initially, the search space solutions are coded using the binary alphabet for a discrete search space. Even though the underlying objective function is a continuous function, genetic algorithms convert the search space into discrete set of points. In order to obtain the optimum point with a desired accuracy, strings of sufficient length need to be