In this chapter, we have introduced the concept of genetic algorithms (GAs) and programming constructs related to GAs. These algorithms derive inspiration from the natural process of evolution. Living species evolve by inheritance, variation in partner selection, and hence attributes of the offspring and occasional (random) mutation in the genetic code (DNA structure). The same concepts are applied in the GAs in order to search the best possible solution from a vast space of possible options. The algorithm is best applied to problems where brute force is insufficient and cannot reach a solution within a reasonable time.
We have seen the structure of GAs in general and implemented a solution for a simple problem in Java. We have reviewed some of the features of the KEEL framework and how it is very easy to translate data into knowledge. KEEL is a Java-based desktop application that facilitates the analysis of the behavior of evolutionary learning in different areas of learning and...