Created by W.Langdon from gp-bibliography.bib Revision:1.7546
In this dissertation we propose a radically different approach, which is based on a new semantic framework that performs effectively against bloat, thus preserving models clarity, and reaching comparable accuracy and generalization performances starting from a common background. More specifically, we introduce the concept of semantics-based equivalence classes. The approach is implemented by means of two different novel genetic programming systems, in which two different definitions of equivalence are used. In both these systems, whenever a solution in an equivalence class is found, it is possible to analytically generate any other solution in that equivalence class. As such, these two systems allow us to shift the objective of genetic programming: instead of finding a globally optimal solution, the objective is to find any solution that belongs to the same equivalence class as a global optimum. Equivalence classes generalize the use of pseudo-distances, as opposed to the traditional use of proper metrics in semantic analysis. Furthermore, we propose improvements to these genetic programming systems in which, once a solution belonging to a particular equivalence class is generated, no other solutions in that class are accepted anymore in the population during the evolution. We call filtered systems these improved versions with respect to efficiency and speed in the solution space exploration phase.
We validated the proposed approach via a experimental results obtained on seven complex real-life test problems. Experimental results show that using equivalence classes is a promising direction and that filters are generally helpful to improve the performance of the systems. Experimental results also show that filters are useful to improve the performance of a state-of-the-art GP method. Indeed, we did an extensive experimentation on the well-known linear scaling technique; this contribution is rather general and can work in synergy with the aforementioned semantic/geometric techniques and with other machine learning frameworks.",
Genetic Programming entries for Stefano Ruberto