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PyTorch Genesis - A Framework for Neuroevolution Neurevolution Using Genetic Programming and Backpropagation

Bibliographic Details
Main Author: Santos, Frederico José Jácome de Brito
Publication Date: 2023
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/148551
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:08:47Zoai:run.unl.pt:10362/148551Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:39:26.852530Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596864892502016

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