This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods ...

This chapter proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution.

Jul 8, 2020 · Optimizing deep neural network architectures is computationally expensive because networks have to be retrained for each fitness evaluation.

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Jun 18, 2019 · This article reviews how evolutionary algorithms have been proposed and tested as a competitive alternative to address a number of issues related to neural ...

This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods ...

This paper describes a multimodal methodology for evolutionary optimization of neural networks. In this approach, we use Differential Evolution with ...

This survey formulate the optimization problems in DNN design such as architecture optimization, hyper-parameter optimization, training and feature ...

In this paper, we propose, for the first time, a new prospect for evolving optimized deep neural networks which can provide a warm start to the training process ...

In this paper, we propose, for the first time, a new prospect for evolving optimized deep neural networks which can provide a warm start to the training process ...