Evolving artificial neural network structure using grammar encoding and colonial competitive algorithm

Mahmoudi, Maryam Tayefeh; Taghiyareh, Fattaneh; Forouzideh, Nafiseh; Lucas, Caro

doi:10.1007/s00521-012-0905-6

Cited by 17 publications

(3 citation statements)

References 32 publications

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“…Neuroevolution of augmenting topologies (NEAT) enables artificial evolution to replicate the formation of brain structures and connections, determine the appropriate number of connections, and eliminate ineffective ones [16]. When DL models are scaled up, the amount of parameters expands rapidly, which poses difficulties to the coding space and computing efficiency problems for the optimization strategies [11,17,18]. To enhance optimization efficiency, we concentrate on employing various neuroevolutionary strategies for NAS.…”

Section: Figure 1: Nas Principlementioning

confidence: 99%

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.

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Section: Figure 1: Nas Principlementioning

confidence: 99%

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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“…To try and solve the need to find a quasi-optimal ANN architecture automatically, Network Architecture Search (NAS) has been extensively researched by using many different approaches but has proven to be a complex task given the dimensions of the search space. Some examples of such approaches include the use of reinforcement learning techniques, as seen in [46][47][48], or the application of evolutive algorithms (e.g., [49][50][51][52][53][54][55][56][57][58][59][60]).…”

Section: Introductionmentioning

confidence: 99%

“…Some systems use grammars to define the rules to obtain valid structures that can encode either the neuron connectivity matrix and some other form of network topology [54,56,57] or other higher level expression of the operations being performed by the network layers and their connectivity [52,53,55,[58][59][60][61]. Some of those systems (e.g., [52,53]) work by generating architecture expressions with high-level operations (such as convolutions, activation functions, dropout, etc.).…”

Section: Introductionmentioning

confidence: 99%

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

Castillo¹,

Díaz-Álvarez²,

Callejo³

et al. 2020

Preprint

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Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

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