In this chapter:
In this chapter we will see how the basic gene expression algorithm can be used to solve problems from very different fields, including symbolic regression, optimization, data mining, time series analysis, classification, logic synthesis, and cellular automata. Furthermore, we will see how to enrich the evolutionary toolkit of GEP through the use of automatically defined functions and user defined functions. And finally, we will also see how the complexity of GEP chromosomes can be easily increased by introducing domains. A chromosomal organization containing an extra domain will be used for the explicit manipulation of numerical constants in GEP. Indeed, this kind of chromosomal organization, which is the cornerstone of GEP induced neural networks, is also used to do parameter optimization, to evolve Kolmogorov-Gabor polynomials, and to evolve decision trees with numerical attributes.