Literature on Neural Architecture Search

@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}

}

@article{https://doi.org/10.1002/int.22471,

title = {License plate recognition using neural architecture search for edge devices},

author = {Jithmi Shashirangana and Heshan Padmasiri and Dulani Meedeniya and Charith Perera and Soumya R Nayak and Janmenjoy Nayak and Shanmuganthan Vimal and Seifidine Kadry},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/int.22471},

doi = {https://doi.org/10.1002/int.22471},

journal = {International Journal of Intelligent Systems},

volume = {n/a},

number = {n/a},

abstract = {Abstract The mutually beneficial blend of artificial intelligence with internet of things has been enabling many industries to develop smart information processing solutions. The implementation of technology enhanced industrial intelligence systems is challenging with the environmental conditions, resource constraints and safety concerns. With the era of smart homes and cities, domains like automated license plate recognition (ALPR) are exploring automate tasks such as traffic management and fraud detection. This paper proposes an optimized decision support solution for ALPR that works purely on edge devices at night-time. Although ALPR is a frequently addressed research problem in the domain of intelligent systems, still they are generally computationally intensive and unable to run on edge devices with limited resources. Therefore, as a novel approach, we consider the complex aspects related to deploying lightweight yet efficient and fast ALPR models on embedded devices. The usability of the proposed models is assessed in real-world with a proof-of-concept hardware design and achieved competitive results to the state-of-the-art ALPR solutions that run on server-grade hardware with intensive resources.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1142/S0129065721500295,

title = {One-Shot Neural Architecture Search by Dynamically Pruning Supernet in Hierarchical Order},

author = {Jianwei Zhang and Dong Li and Lituan Wang and Lei Zhang},

url = {https://doi.org/10.1142/S0129065721500295},

doi = {10.1142/S0129065721500295},

journal = {International Journal of Neural Systems},

abstract = {Neural Architecture Search (NAS), which aims at automatically designing neural architectures, recently draw a growing research interest. Different from conventional NAS methods, in which a large number of neural architectures need to be trained for evaluation, the one-shot NAS methods only have to train one supernet which synthesizes all the possible candidate architectures. As a result, the search efficiency could be significantly improved by sharing the supernet’s weights during the candidate architectures’ evaluation. This strategy could greatly speed up the search process but suffer a challenge that the evaluation based on sharing weights is not predictive enough. Recently, pruning the supernet during the search has been proven to be an efficient way to alleviate this problem. However, the pruning direction in complex-structured search space remains unexplored. In this paper, we revisited the role of path dropout strategy, which drops the neural operations instead of the neurons, in supernet training, and several interesting characters of the supernet trained with dropout are found. Based on the observations, a Hierarchically-Ordered Pruning Neural Architecture Search (HOPNAS) algorithm is proposed by dynamically pruning the supernet with a proper pruning direction. Experimental results indicate that our method is competitive with state-of-the-art approaches on CIFAR10 and ImageNet.},

note = {PMID: 34128778},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sapra2021,

title = {Designing convolutional neural networks with constrained evolutionary piecemeal training},

author = {Dolly Sapra and Andy D. Pimentel },

url = {https://link.springer.com/article/10.1007/s10489-021-02679-7},

journal = {Applied Intelligence },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Artin21,

title = {Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression},

author = {Javad Artin and Amin Valizadeh and Mohsen Ahmadi and Sathish A. P. Kumar and Abbas Sharifi },

url = {https://doi.org/10.1155/2021/8500572},

journal = {Complexity},

abstract = {Traffic prediction is critical to expanding a smart city and country because it improves urban planning and traffic management. This prediction is very challenging due to the multifactorial and random nature of traffic. This study presented a method based on ensemble learning to predict urban traffic congestion based on weather criteria. We used the NAS algorithm, which in the output based on heuristic methods creates an optimal model concerning input data. We had 400 data, which included the characteristics of the day’s weather, including six features: absolute humidity, dew point, visibility, wind speed, cloud height, and temperature, which in the final column is the urban traffic congestion target. We have analyzed linear regression with the results obtained in the project; this method was more efficient than other regression models. This method had an error of 0.00002 in terms of MSE criteria and SVR, random forest, and MLP methods, which have error values of 0.01033, 0.00003, and 0.0011, respectively. According to the MAE criterion, this method has a value of 0.0039. The other methods have obtained values of 0.0850, 0.0045, and 0.027, respectively, which show that our proposed model has a minor error than other methods and has been able to outpace the other models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gong2021,

title = {Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space},

author = {Yunhong Gong and Yanan Sun and Dezhong Peng and Peng Chen and Zhongtai Yan and Ke Yang },

url = {https://link.springer.com/article/10.1007/s40747-021-00513-8},

journal = {Complex & Intelligent Systems },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ma2022,

title = {LPI Radar Waveform Recognition Based on Neural Architecture Search},

author = {Zhiyuan Ma and Wenting Yu and Peng Zhang and Zhi Huang and Anni Lin and Yan Xia},

url = {https://doi.org/10.1155/2022/4628481},

journal = {Computational Intelligence and Neuroscience},

volume = {2022},

keywords = {},

pubstate = {published},

tppubtype = {article}

}