Journal of Machine Learning Research 16 (2015) 673-712 Submitted 11/12; Revised 7/14; Published 4/15 Evolving GPU Machine Code Cleomar Pereira da Silva
[email protected] Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro, RJ 22451-900, Brazil Department of Education Development Federal Institute of Education, Science and Technology - Catarinense (IFC) Videira, SC 89560-000, Brazil Douglas Mota Dias
[email protected] Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro, RJ 22451-900, Brazil Cristiana Bentes
[email protected] Department of Systems Engineering State University of Rio de Janeiro (UERJ) Rio de Janeiro, RJ 20550-013, Brazil Marco Aur´elioCavalcanti Pacheco
[email protected] Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro, RJ 22451-900, Brazil Leandro Fontoura Cupertino
[email protected] Toulouse Institute of Computer Science Research (IRIT) University of Toulouse 118 Route de Narbonne F-31062 Toulouse Cedex 9, France Editor: Una-May O'Reilly Abstract Parallel Graphics Processing Unit (GPU) implementations of GP have appeared in the lit- erature using three main methodologies: (i) compilation, which generates the individuals in GPU code and requires compilation; (ii) pseudo-assembly, which generates the individuals in an intermediary assembly code and also requires compilation; and (iii) interpretation, which interprets the codes. This paper proposes a new methodology that uses the concepts of quantum computing and directly handles the GPU machine code instructions. Our methodology utilizes a probabilistic representation of an individual to improve the global search capability.