Algoritmo Evolucionário com Inspiração Quântica e Sequências Caóticas para Seleção de Atributos em Interfaces Cérebro-Computador
Resumo
Este trabalho descreve a exploração dos efeitos de adicionar ergodicidade a um Algoritmo Evolucionário com inspiração Quântica (QiEA) utilizando sequências caóticas em dois operadores: crossover uniforme caótico e porta quântica de atualização caótica. O algoritmo é aplicado na seleção de atributos de uma aplicação de interface cérebro-computador (BCI) que emprega o eletroencefalograma (EEG) da imaginação do movimento das mãos direita ou esquerda. Os resultados são comparados com os de um QiEA e Algoritmo Genético (GA) tradicionais, demonstrando que o QiEA caótico pode aperfeiçoar significativamente o tempo de convergência do modelo com só uma pequena perda na acurácia final do modelo.
Referências
Celecia, A. et al. (2017) ‘Ensemble of classifiers applied to motor imagery task classification for BCI applications’, in 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 2995–3002. doi: 10.1109/IJCNN.2017.7966227.
Celecia, A., González, R. and Vellasco, M. (2016) ‘Feature Selection Methods Applied to Motor Imagery Task Classification’, in LA-CCI 2016 Latin American Conference on Computational Intelligence. Cartagena, Colombia.
Fuertes, G. et al. (2019) ‘Chaotic genetic algorithm and the effects of entropy in performance optimization’, Chaos. American Institute of Physics Inc., 29(1). doi: 10.1063/1.5048299.
González, R., Vellasco, M. and Figueiredo, K. (2019) ‘Resource optimization for elective surgical procedures using quantum-inspired genetic algorithms’, in Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO ’19. New York, New York, USA: ACM Press, pp. 777–786. doi: 10.1145/3321707.3321786.
Graimann, B., Allison, B. and Pfurtscheller, G. (2009) ‘Brain–Computer Interfaces: A Gentle Introduction’, in Brain-Computer Interfaces. Springer Berlin Heidelberg, pp. 1–27. doi: 10.1007/978-3-642-02091-9_1.
Hui, C., Jiashu, Z. and Chao, Z. (2004) ‘Chaos updating rotated gates quantum-inspired genetic algorithm’, in 2004 International Conference on Communications, Circuits and Systems, pp. 1108–1112. doi: 10.1109/icccas.2004.1346370.
Kuk-Hyun Han and Jong-Hwan Kim (2002) ‘Quantum-inspired evolutionary algorithm for a class of combinatorial optimization’, IEEE Transactions on Evolutionary Computation, 6(6), pp. 580–593. doi: 10.1109/TEVC.2002.804320.
Liu, H. et al. (2010) ‘Feature Selection : An Ever Evolving Frontier in Data Mining’, Journal of Machine Learning Research: Workshop and Conference Proceedings 10: The Fourth Workshop on Feature Selection in Data Mining, pp. 4–13.
Lotte, F. et al. (2018) ‘A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update’, Journal of Neural Engineering. Institute of Physics Publishing. doi: 10.1088/1741-2552/aab2f2.
Lu, H. et al. (2014) ‘The effects of using chaotic map on improving the performance of multiobjective evolutionary algorithms’, Mathematical Problems in Engineering.
Hindawi Publishing Corporation, 2014. doi: 10.1155/2014/924652.
Mozaffari, A. et al. (2017) ‘Comparisons of several variants of continuous quantum-inspired evolutionary algorithms’, Journal of Experimental and Theoretical Artificial Intelligence. Taylor and Francis Ltd., 29(4), pp. 869–909. doi: 10.1080/0952813X.2016.1264088.
Nagashima, H., Baba, Y. and Nakahara, M. (1999) Introduction to Chaos. 1st edn, Introduction to Chaos. 1st edn. CRC Press. doi: 10.1201/9780429187001.
Ramos, A. C. and Vellasco, M. (2018) ‘Quantum-inspired Evolutionary Algorithm for Feature Selection in Motor Imagery EEG Classification’, in 2018 IEEE Congress on Evolutionary Computation, CEC 2018. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/CEC.2018.8477705.
Schlögl, A. (2002) ‘Data set: BCI-experiment’. BCI competition 2003. Available at: https://www.bbci.de/competition/ii/Graz_description.doc.
Schlögl, A. (2005) ‘Dataset IIIb: Non-stationary 2-class BCI data’. BCI Competition III.
Senkerik, R. et al. (2018) ‘On the Population Diversity for the Chaotic Differential Evolution’, in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/CEC.2018.8477741.
Szwarcman, D., Civitarese, D. and Vellasco, M. (2019) ‘Quantum-Inspired Neural Architecture Search’, in Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/IJCNN.2019.8852453.
Ting, W. et al. (2008) ‘EEG feature extraction based on wavelet packet decomposition for brain computer interface’, Measurement, 41(6), pp. 618–625. doi: 10.1016/j.measurement.2007.07.007.
Wang, H. et al. (2013) ‘The improvement of quantum genetic algorithm and its application on function optimization’, Mathematical Problems in Engineering, 2013(1). doi: 10.1155/2013/730749.
Zhang, G. (2011) ‘Quantum-inspired evolutionary algorithms: a survey and empirical study’, Journal of Heuristics. Springer US, 17(3), pp. 303–351. doi: 10.1007/s10732-010-9136-0.
Zhao, S. et al. (2009) ‘Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks’, Computers and Mathematics with Applications, 57(11–12), pp. 2009–2015. doi: 10.1016/j.camwa.2008.10.048.
Celecia, A., González, R. and Vellasco, M. (2016) ‘Feature Selection Methods Applied to Motor Imagery Task Classification’, in LA-CCI 2016 Latin American Conference on Computational Intelligence. Cartagena, Colombia.
Fuertes, G. et al. (2019) ‘Chaotic genetic algorithm and the effects of entropy in performance optimization’, Chaos. American Institute of Physics Inc., 29(1). doi: 10.1063/1.5048299.
González, R., Vellasco, M. and Figueiredo, K. (2019) ‘Resource optimization for elective surgical procedures using quantum-inspired genetic algorithms’, in Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO ’19. New York, New York, USA: ACM Press, pp. 777–786. doi: 10.1145/3321707.3321786.
Graimann, B., Allison, B. and Pfurtscheller, G. (2009) ‘Brain–Computer Interfaces: A Gentle Introduction’, in Brain-Computer Interfaces. Springer Berlin Heidelberg, pp. 1–27. doi: 10.1007/978-3-642-02091-9_1.
Hui, C., Jiashu, Z. and Chao, Z. (2004) ‘Chaos updating rotated gates quantum-inspired genetic algorithm’, in 2004 International Conference on Communications, Circuits and Systems, pp. 1108–1112. doi: 10.1109/icccas.2004.1346370.
Kuk-Hyun Han and Jong-Hwan Kim (2002) ‘Quantum-inspired evolutionary algorithm for a class of combinatorial optimization’, IEEE Transactions on Evolutionary Computation, 6(6), pp. 580–593. doi: 10.1109/TEVC.2002.804320.
Liu, H. et al. (2010) ‘Feature Selection : An Ever Evolving Frontier in Data Mining’, Journal of Machine Learning Research: Workshop and Conference Proceedings 10: The Fourth Workshop on Feature Selection in Data Mining, pp. 4–13.
Lotte, F. et al. (2018) ‘A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update’, Journal of Neural Engineering. Institute of Physics Publishing. doi: 10.1088/1741-2552/aab2f2.
Lu, H. et al. (2014) ‘The effects of using chaotic map on improving the performance of multiobjective evolutionary algorithms’, Mathematical Problems in Engineering.
Hindawi Publishing Corporation, 2014. doi: 10.1155/2014/924652.
Mozaffari, A. et al. (2017) ‘Comparisons of several variants of continuous quantum-inspired evolutionary algorithms’, Journal of Experimental and Theoretical Artificial Intelligence. Taylor and Francis Ltd., 29(4), pp. 869–909. doi: 10.1080/0952813X.2016.1264088.
Nagashima, H., Baba, Y. and Nakahara, M. (1999) Introduction to Chaos. 1st edn, Introduction to Chaos. 1st edn. CRC Press. doi: 10.1201/9780429187001.
Ramos, A. C. and Vellasco, M. (2018) ‘Quantum-inspired Evolutionary Algorithm for Feature Selection in Motor Imagery EEG Classification’, in 2018 IEEE Congress on Evolutionary Computation, CEC 2018. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/CEC.2018.8477705.
Schlögl, A. (2002) ‘Data set: BCI-experiment’. BCI competition 2003. Available at: https://www.bbci.de/competition/ii/Graz_description.doc.
Schlögl, A. (2005) ‘Dataset IIIb: Non-stationary 2-class BCI data’. BCI Competition III.
Senkerik, R. et al. (2018) ‘On the Population Diversity for the Chaotic Differential Evolution’, in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/CEC.2018.8477741.
Szwarcman, D., Civitarese, D. and Vellasco, M. (2019) ‘Quantum-Inspired Neural Architecture Search’, in Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/IJCNN.2019.8852453.
Ting, W. et al. (2008) ‘EEG feature extraction based on wavelet packet decomposition for brain computer interface’, Measurement, 41(6), pp. 618–625. doi: 10.1016/j.measurement.2007.07.007.
Wang, H. et al. (2013) ‘The improvement of quantum genetic algorithm and its application on function optimization’, Mathematical Problems in Engineering, 2013(1). doi: 10.1155/2013/730749.
Zhang, G. (2011) ‘Quantum-inspired evolutionary algorithms: a survey and empirical study’, Journal of Heuristics. Springer US, 17(3), pp. 303–351. doi: 10.1007/s10732-010-9136-0.
Zhao, S. et al. (2009) ‘Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks’, Computers and Mathematics with Applications, 57(11–12), pp. 2009–2015. doi: 10.1016/j.camwa.2008.10.048.
Publicado
29/11/2021
Como Citar
CELECIA, Alimed; VELLASCO, Marley.
Algoritmo Evolucionário com Inspiração Quântica e Sequências Caóticas para Seleção de Atributos em Interfaces Cérebro-Computador. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 386-397.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2021.18269.
