Towards the development of a complete GP system on an FPGA using geometric semantic operators

Abstract

Genetic Programming (GP) has been around for over two decades and has been used in a wide range of practical applications producing human competitive results in several domains. In this paper we present a discussion and a proposal of a GP algorithm that could be conveniently implemented on an embedded system, as part of a broader research project that pursues the implementation of a complete GP system in a Field Programmable Gate Array (FPGA). Motivated by the significant time savings associated with such a platform, as well as low power consumption, low maintenance requirements, small size of the system and the possibility of performing several parallel processes. The proposal is focused on the Geometric Semantic Genetic Programming (GSGP) approach that has been recently introduced with promising results. GSGP induces a unimodal fitness landscape, simplifying the search process. The experimental work considers five variants of GSGP, that incorporate local search strategies, optimal mutations and alignment in error space. Best results were obtained by a simple variant that uses both the optimal mutation step and the standard geometric semantic mutation, using three difficult real-world problems to evaluate the methods, outperforming the original GSGP formulation in terms of fitness and empirical convergence.