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Frankenstein PSO applied to neural network weights and architectures., and . IEEE Congress on Evolutionary Computation, page 2452-2456. IEEE, (2011)Quantum probabilistic associative memory architecture., , , and . Neurocomputing, (2019)Hybrid K-Means and Improved Self-Adaptive Particle Swarm Optimization for Data Clustering., and . IJCNN, page 1-7. IEEE, (2019)A hybrid evolutionary decomposition system for time series forecasting., and . Neurocomputing, (2016)An approach to reservoir computing design and training., , and . Expert Syst. Appl., 40 (10): 4172-4182 (2013)Selection of Models for Time Series Prediction via Meta-Learning., and . HIS, volume 87 of Frontiers in Artificial Intelligence and Applications, page 74-83. IOS Press, (2002)Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!, , and . CoRR, (2022)Distinction Maximization Loss: Efficiently Improving Classification Accuracy, Uncertainty Estimation, and Out-of-Distribution Detection Simply Replacing the Loss and Calibrating., , and . CoRR, (2022)A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations., , , and . CoRR, (2023)Uso de GSO cooperativos com decaimentos de pesos para otimizacao de redes neurais., and . CoRR, (2021)