The Basic GEA in Problem Solving

In this chapter we will see how the basic gene expression algorithm can be used to solve complex problems from very different fields. We will start by exploring the workings of the fundamental parameters of the algorithm by solving a simple cubic polynomial. Then we will continue our exploration by analyzing the performance of the algorithm on a complex test function of five arguments. Furthermore, we will also discuss the power of the algorithm to extract knowledge from noisy data, not only by mining a noisy computer- generated dataset but also by mining complex real-world data, including mining a dataset with 51 input attributes in order to decide whether to approve or not a credit card. We will also see how the basic GEA can be used to diagnose diseases and classify different types of plants. Particularly interesting is the three-class prediction of the iris data that will be tackled using two different approaches: the first consisting of the conventional way of partitioning the data into three separate datasets so that three different models are created and afterwards combined to make the final prediction; and the second consisting of a three-genic system evolving three different models at the same time, in which each model is responsible for classifying a certain type of plant.