Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Schuetze, Oliver"'
Autor:
Wang, Hao, Rodriguez-Fernandez, Angel E., Uribe, Lourdes, Deutz, André, Cortés-Piña, Oziel, Schütze, Oliver
A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algorithms have proven to be very e
Externí odkaz:
http://arxiv.org/abs/2405.05721
Neural networks and deep learning are changing the way that artificial intelligence is being done. Efficiently choosing a suitable network architecture and fine-tune its hyper-parameters for a specific dataset is a time-consuming task given the stagg
Externí odkaz:
http://arxiv.org/abs/1905.06010
Publikováno v:
Neural Networks 116, (2019) 178-187
This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use
Externí odkaz:
http://arxiv.org/abs/1905.05918
Publikováno v:
In Applied Soft Computing Journal March 2023 136
Publikováno v:
In Applied Soft Computing Journal August 2022 125
Publikováno v:
In Swarm and Evolutionary Computation December 2021 67
Publikováno v:
In Swarm and Evolutionary Computation March 2020 53
Publikováno v:
In Swarm and Evolutionary Computation February 2020 52
Publikováno v:
In Swarm and Evolutionary Computation February 2020 52
Recently, a framework for the approximation of the entire set of $\epsilon$-efficient solutions (denote by $E_\epsilon$) of a multi-objective optimization problem with stochastic search algorithms has been proposed. It was proven that such an algorit
Externí odkaz:
http://arxiv.org/abs/0804.0581