- author = "Carlos Goribar-Jimenez and Yazmin Maldonado and Leonardo Trujillo and Mauro Castelli and Ivo Goncalves and Leonardo Vanneschi",
- booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
- title = "Towards the development of a complete GP system on an FPGA using geometric semantic operators",
- year = "2017",
- editor = "Jose A. Lozano",
- pages = "1932--1939",
- address = "Donostia, San Sebastian, Spain",
- month = "5-8 " # jun,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, convergence, embedded systems, field programmable gate arrays, geometry, parallel processing, search problems, FPGA, GP algorithm, GP system development, GSGP, embedded system, empirical convergence, error space alignment, field programmable gate array, geometric semantic genetic programming, geometric semantic operators, local search strategies, maintenance requirements, optimal mutation step, parallel processes, power consumption, standard geometric semantic mutation, time savings, unimodal fitness landscape, Arrays, GSM, Proposals, Semantics, Standards",
- isbn13 = "978-1-5090-4601-0",
-
DOI = "
doi:10.1109/CEC.2017.7969537",
- size = "8 pages",
- 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.",
-
notes = "predict the energy consumption of residential
buildings HAVC. UCI Housing
IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969537}",
