Genetic Programming (GP) is a powerful software induction technique that can be applied to solve a wide variety of problems. However, most researchers develop tailor-made GP tools for solving specific problems. These tools generally require significant modifications in their kernel to be adapted to other domains. In this paper, we explore the Grammar-Guided Genetic Programming (GGGP) approach as an alternative to overcome such limitation. We describe a GGGP based framework, named Chameleon, that can be easily configured to solve different problems. We explore the use of Chameleon in two domains, not usually addressed by works in the literature: in the task of mining relational databases and in the software testing activity. The presented results point out that the use of the grammar-guided approach helps us to obtain more generic GP frameworks and that they can contribute in the explored domains.
GSGP-C++ 2.0: A geometric semantic genetic programming framework
