Evolving MIMO multi-layered artificial neural networks using grammatical evolution

Ahmad, Qadeer; Rafiq, Atif; Raja, Muhammad Adil; Javed, Noman

doi:10.1145/3297280.3297408

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…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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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

View full text Add to dashboard Cite

show abstract

Bio-Inspired System for Electricity Price Forecast in the Brazilian Market

Campos

Pereira

Duarte

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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

et al. 2020

View full text Add to dashboard Cite

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 is 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 main goal is the automatic design of deep neural network architectures with grammar-guided genetic programming. 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.

show abstract

Evolving MIMO multi-layered artificial neural networks using grammatical evolution

Cited by 3 publications

References 24 publications

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

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

Bio-Inspired System for Electricity Price Forecast in the Brazilian Market

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

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