Grammar-based Neuroevolution of Fully Convolutional Networks

• Ano de defesa: 2025
• Autor(a) principal: Miranda, Thiago Zafalon
• Orientador(a): Cerri, Ricardo
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Inglês: evolutionary algorithm; neural network; neuroevolution; multi-objective optimization; grammatical evolution; image classification
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO
• Link de acesso: https://hdl.handle.net/20.500.14289/22897

The design of complex and deep neural networks is often performed by identifying and combining building blocks and progressively selecting the most promising combination. Neuroevolution automates this process by employing evolutionary algorithms to guide the search. Within this field, grammar-based evolutionary algorithms have been demonstrated to be powerful tools to describe and thus encode complex neural architectures effectively. In this context, this research proposes a novel grammar-based multi-objective neuroevolutionary approach for generating fully convolutional networks. The proposed method, named Multi-Objective gRammatical Evolution for FUlly convolutional Networks (MOREFUN), includes a new efficient way to encode skip-connections, facilitating the description of complex search spaces and the injection of domain knowledge in the search procedure, the generation of fully convolutional networks upsampling of lower-resolution inputs in multi-input layers, the usage of multi-objective fitness, and the inclusion of data augmentation and optimizer settings in the grammar. The best networks found by the algorithm outperformed those generated by previous grammar-based evolutionary algorithms, achieving 90% accuracy on CIFAR-10 without using transfer learning, ensembles, or test-time data augmentation, while having a relatively small number of parameters.