Evolving intelligence: Overcoming challenges for Evolutionary Deep Learning

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Altarabichi, Mohammed Ghaith,1981-Högskolan i Halmstad,Akademin för informationsteknologi (author)

Evolving intelligence : Overcoming challenges for Evolutionary Deep Learning

BookEnglish2024

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Halmstad :Halmstad University Press,2024
32 s.
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:hh-52469
ISBN:9789189587311
ISBN:9789189587328
https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-52469URI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:vet swepub-contenttype
Subject category:dok swepub-publicationtype

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Halmstad University Dissertations ;109

Notes

Deep Learning (DL) has achieved remarkable results in both academic and industrial fields over the last few years. However, DL models are often hard to design and require proper selection of features and tuning of hyper-parameters to achieve high performance. These selections are tedious for human experts and require substantial time and resources. A difficulty that encouraged a growing number of researchers to use Evolutionary Computation (EC) algorithms to optimize Deep Neural Networks (DNN); a research branch called Evolutionary Deep Learning (EDL).This thesis is a two-fold exploration within the domains of EDL, and more broadly Evolutionary Machine Learning (EML). The first goal is to makeEDL/EML algorithms more practical by reducing the high computational costassociated with EC methods. In particular, we have proposed methods to alleviate the computation burden using approximate models. We show that surrogate-models can speed up EC methods by three times without compromising the quality of the final solutions. Our surrogate-assisted approach allows EC methods to scale better for both, expensive learning algorithms and large datasets with over 100K instances. Our second objective is to leverage EC methods for advancing our understanding of Deep Neural Network (DNN) design. We identify a knowledge gap in DL algorithms and introduce an EC algorithm precisely designed to optimize this uncharted aspect of DL design. Our analytical focus revolves around revealing avant-garde concepts and acquiring novel insights. In our study of randomness techniques in DNN, we offer insights into the design and training of more robust and generalizable neural networks. We also propose, in another study, a novel survival regression loss function discovered based on evolutionary search.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Datorsystem hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
neural networks
evolutionary deep learning
evolutionary machine learning
feature selection
hyperparameter optimization
evolutionary computation
particle swarm optimization
genetic algorithm

Added entries (persons, corporate bodies, meetings, titles ...)

Nowaczyk, Sławomir,Professor,1978-Högskolan i Halmstad,Akademin för informationsteknologi(Swepub:hh)slanow (thesis advisor)
Pashami, Sepideh,Associate Professor,1985-Högskolan i Halmstad,Akademin för informationsteknologi(Swepub:hh)seppas (thesis advisor)
Sheikholharam Mashhadi, Peyman,Senior Lecturer,1982-Högskolan i Halmstad,Akademin för informationsteknologi(Swepub:hh)peymas (thesis advisor)
Lavesson, Niklas,Professor,1985-Blekinge Institute of Technology, Karlskrona, Sweden(Swepub:hh)seppas (opponent)
Högskolan i HalmstadAkademin för informationsteknologi (creator_code:org_t)

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