Programação Genética Linear com Inspiração Quântica
Resumo
Algoritmos evolutivos com inspiração quântica (AEIQ) aproveitam princípios da mecânica quântica para melhorar o desempenho de algoritmos evolutivos clássicos. Este artigo apresenta um novo modelo de AEIQ (“Programação Genética Linear com Inspiração Quântica” – PGLIQ) para evoluir programas em código de máquina. Nos testes comparativos são utilizados problemas de regressão simbólica e o modelo atual mais eficiente na evolução de código de máquina (AIMGP). A PGLIQ apresenta desempenho superior, obtendo melhores soluções com menos avaliações, parâmetros e operadores. Conclui-se que a inspiração quântica pode ser uma abordagem competitiva para se evoluir programas mais eficientemente.Referências
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Nordin, P. (1998). AIMGP: A formal description. In Koza, J. R., editor, Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin, Madison, Wisconsin, USA. Stanford University Bookstore.
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Abs da Cruz, A., Vellasco, M., and Pacheco, M. (2010). Quantum-inspired evolutionary algorithms applied to numerical optimization problems. In IEEE Congress on Evolutionary Computation (CEC 2010), pages 1–6.
Brameier, M. and Banzhaf, W. (2007). Linear Genetic Programming. Number XVI in Genetic and Evolutionary Computation. Springer, Boston, MA, USA.
Dias, D. M. and Pacheco, M. A. C. (2009). Toward a quantum-inspired linear genetic programming model. In Tyrrell, A., editor, IEEE Congress on Evolutionary Computation, pages 1691–1698, Trondheim, Norway. IEEE Press.
Dias, D. M., Pacheco, M. A. C., and Amaral, J. F. M. (2006). Automatic synthesis of microcontroller assembly code through linear genetic programming. In Nedjah, N., Abraham, A., and de Macedo Mourelle, L., editors, Genetic Systems Programming: Theory and Experiences, volume 13 of Studies in Computational Intelligence, pages 195–234. Springer, Germany.
Francone, F. D. (2010). Discipulus Owner’s Manual. Register Machine Learning Technologies. [link].
Han, K.-H. and Kim, J.-H. (2002). Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. Evolutionary Computation, IEEE Transactions on, 6(6):580–593.
Kordon, A. (2010). Symbolic regression competition – EvoStar Conference. [link]. Dow Chemical (USA).
Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.
Koza, J. R. (2010). Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines, 11(3-4):251–284.
Lanyon, B., Barbieri, M., Almeida, M., Jennewein, T., Ralph, T., Resch, K., Pryde, G., O’Brien, J., Gilchrist, A., and White, A. (2008). Quantum computing using shortcuts through higher dimensions. Arxiv preprint arXiv:0804.0272.
Lavor, C., Manssur, L., and Portugal, R. (2003). Shor’s algorithm for factoring large integers. Arxiv preprint quant-ph/0303175.
Moore, M. and Narayanan, A. (1995). Quantum-inspired computing. Dept. Comput. Sci., Univ. Exeter.
Nordin, P. (1998). AIMGP: A formal description. In Koza, J. R., editor, Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin, Madison, Wisconsin, USA. Stanford University Bookstore.
Poli, R., Langdon, W. B., and McPhee, N. F. (2008). A field guide to genetic programming. Published via [link]. (With contributions by J. R. Koza).
Singulani, A., Vilela Neto, O., Pacheco, M., Vellasco, M., Pires, M., and Souza, P. (2008). Computational intelligence applied to the growth of quantum dots. Journal of Crystal Growth, 310(23):5063–5065.
Publicado
19/07/2011
Como Citar
DIAS, Douglas Mota; PACHECO, Marco Aurélio C..
Programação Genética Linear com Inspiração Quântica. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 8. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
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p. 665-676.
ISSN 2763-9061.