A New Grammatical Evolution Method for Generating Deep Convolutional Neural Networks with Novel Topologies

Autor(es)

Miranda, Thiago Zafalon ; Sardinha, Diorge Brognara ; Basgalupp, Marcio Porto ; Cerri, Ricardo ; ACM

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022; v. N/A, p. 4-pg., 2022-01-01.

Resumo

Neuroevolution, a sub-field of AutoML, utilizes evolutionary algorithms to automate the process of creating Deep Neural Networks architectures. For problems with complex objects, such as neural networks, Grammar-based Evolutionary Algorithms (GEs) can be used to simplify the implementation and the experimentation by using grammar rules to describe what the components of the complex object are and how they can be connected, that is, they elegantly describe the search space of the problem. In this work, we propose a GE algorithm based on Structured Grammatical Evolution to generate deep convolutional neural networks. Our work has two major contributions: first, the neural networks may contain an arbitrary number and arrangement of skip connections; second, our skip connections may upscale lower-resolution inputs, allowing the generation of architectures such as U-Net. Our best model achieved 0.85 accuracy on CIFAR-10. (AU)

• Processo FAPESP: 22/03590-2 - GECCO 2022 - The Genetic and Evolutionary Computation Conference
• Beneficiário: Márcio Porto Basgalupp
• Modalidade de apoio: Auxílio à Pesquisa - Reunião - Exterior
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 20/09835-1 - IARA - Inteligência Artificial Recriando Ambientes
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia