• Function finding on a one-dimensional parameter space
• Function finding on a five-dimensional parameter space
• Mining meaningful information from noisy data

We have already seen how gene expression programming can be used to do symbolic regression in the simple example of section 3.4. Here, we will analyze more complex problems of symbolic regression. The first is a simple test function exactly solved by the algorithm and, therefore, ideal for showing the workings of the fundamental parameters of the algorithm. The second consists of a complex test function that shows how GEP can be efficiently applied to model complex realities with great accuracy. And the last is used to illustrate how gene expression programming can be efficiently used for mining relevant information from noisy data.