PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/148551 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
id |
RCAP_66008d64abfbaf56a4fbcb7d007f81a4 |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/148551 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and BackpropagationNeuroevolutionEvolutionary Deep LearningNeural Architecture SearchSupervised LearningDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceSelf-evolving neural networks have been long sought after. Evolutionary Deep Learning is an emerging field full of exciting research bringing together the fields of Deep Learning and Evolutionary Computation. The objective of this thesis is to develop a method of evolving deep neural networks by adapting the theory behind Geometric Semantic Genetic Programming, a subfield of Genetic Programming, and Semantic Learning Machine. Our method evolves neural networks by incrementing their number of neurons throughout generations, whilst using backpropagation for the optimization of the network’s parameters. We bring together evolution through natural selection and the advances in optimization through backpropagation in the field of deep learning. We evolve neural networks that achieve nearly 90% accuracy on the CIFAR-10 dataset with a relatively low number of parameters, evolving in GPU-minutes vs the field standard of GPU-days. We develop PyTorch Genesis, a framework to evolve these models with the hope of opening the gates to a different way of evolving neural networks.Castelli, MauroGonçalves, Ivo Carlos PereiraRUNSantos, Frederico José Jácome de Brito2023-01-232026-01-23T00:00:00Z2023-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/148551TID:203210387enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T18:08:47Zoai:run.unl.pt:10362/148551Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:08:47Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation |
title |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation |
spellingShingle |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation Santos, Frederico José Jácome de Brito Neuroevolution Evolutionary Deep Learning Neural Architecture Search Supervised Learning |
title_short |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation |
title_full |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation |
title_fullStr |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation |
title_full_unstemmed |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation |
title_sort |
PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation |
author |
Santos, Frederico José Jácome de Brito |
author_facet |
Santos, Frederico José Jácome de Brito |
author_role |
author |
dc.contributor.none.fl_str_mv |
Castelli, Mauro Gonçalves, Ivo Carlos Pereira RUN |
dc.contributor.author.fl_str_mv |
Santos, Frederico José Jácome de Brito |
dc.subject.por.fl_str_mv |
Neuroevolution Evolutionary Deep Learning Neural Architecture Search Supervised Learning |
topic |
Neuroevolution Evolutionary Deep Learning Neural Architecture Search Supervised Learning |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01-23 2023-01-23T00:00:00Z 2026-01-23T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/148551 TID:203210387 |
url |
http://hdl.handle.net/10362/148551 |
identifier_str_mv |
TID:203210387 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
_version_ |
1817545913583796224 |