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Correspondence between neuroevolution and gradient descent [PDF]
Nature Communications, 2021 We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise ...
Stephen Whitelam +3 more
doaj +3 more sources
Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection [PDF]
Entropy, 2021 Multivariate time series anomaly detection is a widespread problem in the field of failure prevention. Fast prevention means lower repair costs and losses.
Marcin Pietron +2 more
exaly +4 more sources
Towards the Neuroevolution of Low-level artificial general intelligence [PDF]
Frontiers in Robotics and AI, 2022 In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding ...
Anis Yazidi, Stefano Nichele
exaly +4 more sources
Improving the performance of mutation-based evolving artificial neural networks with self-adaptive mutations. [PDF]
PLoS ONENeuroevolution is a promising approach for designing artificial neural networks using an evolutionary algorithm. Unlike recent trending methods that rely on gradient-based algorithms, neuroevolution can simultaneously evolve the topology and weights of ...
Motoaki Hiraga +4 more
doaj +2 more sources
Neuroevolution for Parameter Adaptation in Differential Evolution
Algorithms, 2022 Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is ...
Vladimir Stanovov +2 more
exaly +3 more sources
Neuroevolution Guided Hybrid Spiking Neural Network Training [PDF]
Frontiers in Neuroscience, 2022 Neuromorphic computing algorithms based on Spiking Neural Networks (SNNs) are evolving to be a disruptive technology driving machine learning research.
Sen Lu, Abhronil Sengupta
doaj +2 more sources
Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies
Algorithms, 2021 In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for ...
Vladimir Stanovov +2 more
exaly +3 more sources
Mathematical and Computational Applications, 2023 Breast cancer has become a global health problem, ranking first in incidences and fifth in mortality in women around the world. In Mexico, the first cause of death in women is breast cancer. This work uses deep learning techniques to discriminate between
Héctor-Gabriel Acosta-Mesa +2 more
exaly +3 more sources
Applied Sciences (Switzerland), 2020 The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required.
José Francisco Torres Maldonado +2 more
exaly +3 more sources
Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution
Vehicles, 2022 The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains.
Frederic Jacquelin +6 more
doaj +1 more source