Summary

In this chapter, we learned the basic concepts of SC to exploit the tolerance of imprecision, uncertainty, and rough reasoning to achieve behavior like human decision-making. We analyzed the basic techniques of SC: fuzzy logic, neural networks, evolutionary computation, and GAs. We understood how these technologies can be exploited to support us in our choices. We then deepened the concepts behind GAs and saw how genetic programming based on human evolution can be valuable in the optimization of processes.

Subsequently, we learned how to use GA techniques to implement SR. Symbolic equations represent an important resource for scientific research. The identification of a mathematical model that can represent a complete system is far from an easy task. The use of data can come to our aid; in fact, through SR, we can discover the equation underlying a set of input-output pairs. After analyzing the basics of the SR procedure through genetic programming, we saw a practical case...