Neural and evolutionary computing

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Model-based evolutionary computing: a neural network and genetic algorithm architecture

Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), 2002
Traditional evolutionary computing uses an explicit fitness function-mathematical or simulated-to derive a solution to a problem from a population of individuals, over a number of generations. In this paper an architecture is presented which allows such techniques to be used on problems which cannot be expressed mathematically or which are difficult to
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Evolutionary Computation for Designing Deep Recurrent Neural Networks

<p dir="ltr">Recurrent Neural Networks (RNNs) are a major class of Artificial Neural Networks (ANNs). Suitable network architecture is vital for RNNs to achieve high performance. However, designing the architecture of deep RNNs (DRNNs) is a complicated and time-consuming process. Each layer of a DRNN contains numerous hyper-parameters.
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Intrusion Detection Using Multi-Objective Evolutionary Convolutional Neural Network for Internet of Things in Fog Computing

Knowledge-Based Systems, 2022
Ka Chun Wong, Wenhong Wei, Junkai Ji
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Neural Network Based Multi-Objective Evolutionary Algorithm for Dynamic Workflow Scheduling in Cloud Computing

Future Generation Computer Systems, 2020
Halûk Rahmi Topcuoglu
exaly

Neural and Evolutionary Computing

2016
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Evolutionary Neural Networks and DNA Computing Algorithms for Dual-Axis Motion Control

Engineering Applications of Artificial Intelligence, 2011
Chun-Liang Lin
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Architectural Space Planning Using Evolutionary Computing Approaches: A Review

Artificial Intelligence Review, 2011
Kamlesh Dutta
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A Survey of Swarm and Evolutionary Computing Approaches for Deep Learning

Artificial Intelligence Review, 2020
Swagatam Das +2 more
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