@article{Stanley2009_mid,
title = {A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks},
author = {Kenneth O Stanley and David B D'Ambrosio and Jason Gauci},
url = {https://ieeexplore.ieee.org/document/6792316/},
doi = {10.1162/ARTL.2009.15.2.15202},
year = {2009},
date = {2009-01-01},
volume = {15},
number = {2},
pages = {185-212},
key = {journals/alife/StanleyDG09},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Floreano2008_wpi,
title = {Neuroevolution - from architectures to learning},
author = {Dario Floreano and Peter Dürr and Claudio Mattiussi},
url = {https://link.springer.com/article/10.1007/s12065-007-0002-4},
doi = {10.1007/S12065-007-0002-4},
year = {2008},
date = {2008-01-01},
volume = {1},
number = {1},
pages = {47-62},
key = {journals/evi/FloreanoDM08},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Roy2004_app,
title = {Designing Polymer Blends Using Neural Networks, Genetic Algorithms, and Markov Chains},
author = {N K Roy and Walter D Potter and D P Landau},
url = {https://dl.acm.org/citation.cfm?id=94034},
doi = {10.1023/B:APIN.0000021414.50728.34},
year = {2004},
date = {2004-01-01},
volume = {20},
number = {3},
pages = {215-229},
key = {journals/apin/RoyPL04},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Angeline1994_qts,
title = {An evolutionary algorithm that constructs recurrent neural networks},
author = {Peter J Angeline and Gregory M Saunders and Jordan B Pollack},
url = {https://ieeexplore.ieee.org/document/265960/},
doi = {10.1109/72.265960},
year = {1994},
date = {1994-01-01},
volume = {5},
number = {1},
pages = {54-65},
key = {journals/tnn/AngelineSP94},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Kitano1990_uli,
title = {Designing Neural Networks Using Genetic Algorithms with Graph Generation System},
author = {Hiroaki Kitano},
url = {http://www.complex-systems.com/abstracts/v04_i04_a06/},
year = {1990},
date = {1990-01-01},
volume = {4},
number = {4},
key = {journals/compsys/Kitano90},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{https://doi.org/10.1111/exsy.12690,
title = {Hybrid evolutionary network architecture search (HyENAS) for convolution class of deep neural networks with applications},
author = {Soniya and Lotika Singh and Sandeep Paul},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12690},
doi = {https://doi.org/10.1111/exsy.12690},
journal = {Expert Systems},
volume = {n/a},
number = {n/a},
pages = {e12690},
abstract = {Abstract Convolutional Neural Networks (CNNs) and its variants are increasingly used across wide domain of applications achieving high performance measures. For high performance, application specific CNN architecture is required, hence the need for network architecture search (NAS) becomes essential. This paper proposes a hybrid evolutionary approach for network architecture search (HyENAS), and targets convolution class of neural networks. One of the significant contribution of this technique is to completely evolve the high performance network by simultaneously finding network structures and their corresponding parameters. An elegant string representation has been proposed which efficiently represents the network. The concept of sparse block evolving requisite layer wise features for dense network is deployed. This permits the network to dynamically evolve for a specific application. In comparison to the other state-of-art methods, the high performance of the proposed HyENAS approach is demonstrated across various benchmark data sets belonging to the domain of malariology, oncology, neurology, ophthalmology, and genomics. Further, to deploy the proposed model on lower hardware specification devices, another salient feature of the HyENAS technique is to seamlessly sift out the simpler network architecture with comparable accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{https://doi.org/10.1049/cvi2.12036,
title = {Control the number of skip-connects to improve robustness of the NAS algorithm},
author = {Bao Feng Zhang and Guo Qiang Zhou},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/cvi2.12036},
doi = {https://doi.org/10.1049/cvi2.12036},
journal = {IET Computer Vision},
volume = {n/a},
number = {n/a},
abstract = {Abstract Recently, the gradient-based neural architecture search has made remarkable progress with the characteristics of high efficiency and fast convergence. However, two common problems in the gradient-based NAS algorithms are found. First, with the increase in the raining time, the NAS algorithm tends to skip-connect operation, leading to performance degradation and instability results. Second, another problem is no reasonable allocation of computing resources on valuable candidate network models. The above two points lead to the difficulty in searching the optimal sub-network and poor stability. To address them, the trick of pre-training the super-net is applied, so that each operation has an equal opportunity to develop its strength, which provides a fair competition condition for the convergence of the architecture parameters. In addition, a skip-controller is proposed to ensure each sampled sub-network with an appropriate number of skip-connects. The experiments were performed on three mainstream datasets CIFAR-10, CIFAR-100 and ImageNet, in which the improved method achieves comparable results with higher accuracy and stronger robustness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}