Results 51 to 60 of about 1,570 (213)
Scaling MAP-Elites to deep neuroevolution [PDF]
Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020 Accepted to GECCO ...
Colas, Cédric +3 more
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Genotype dynamic for agent neuroevolution in artificial life model
Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï, 2017 Cooperation behavior is one of the most used and spread Multi-agent system feature. In some cases emergence of this behaviour can be characterized by division of population on co-evolving subpopulations [1], [2].
Valentine V. Zavertanyy, A. S. Makarenko
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Application of Neuroevolution in Autonomous Cars [PDF]
, 2021 With the onset of Electric vehicles, and them becoming more and more popular, autonomous cars are the future in the travel/driving experience. The barrier to reaching level 5 autonomy is the difficulty in the collection of data that incorporates good driving habits and the lack thereof.
G. Sainath +3 more
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Incorporating Advice into Neuroevolution of Adaptive Agents
, 2021 Neuroevolution is a promising learning method in tasks with extremely large state and action spaces and hidden states. Recent advances allow neuroevolution to take place in real time, making it possible to e.g. construct video games with adaptive agents.
Yong, Chern +3 more
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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
José-Luis Llaguno-Roque +4 more
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Towards Behavioral Consistency in Neuroevolution [PDF]
, 2012 To survive in its environment, an animat must have a be-havior that is not too disturbed by noise or any other distractor. Itsbehavior is supposed to be relatively unchanged when testedon similarsituations. Evolving controllers that are robust and generalize well oversimilar contexts remains a challenge for several reasons.
Ollion, Charles, Doncieux, Stéphane
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Applied Sciences, 2022 Artificial neural networks have proven to be effective in a wide range of fields, providing solutions to various problems. Training artificial neural networks using evolutionary algorithms is known as neuroevolution.
Sabina-Adriana Floria +3 more
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Robust optimization through neuroevolution
PLOS ONE, 2019 We propose a method for evolving solutions that are robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The obtained results show how the method proposed is effective and computational tractable. It permits to improve performance on
Pagliuca P, Nolfi S
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Sensors, 2022 Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications.
Baris Baykant Alagoz +5 more
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Neuroevolution untuk optimalisasi parameter jaringan saraf tiruan
, 2023 Artificial Neural Network is a supervised learning method for various classification problems. Artificial Neural Network uses training data to identify patterns in the data; therefore, training phase is crucial.
Purnomo, Hindriyanto Dwi +3 more
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