Sökning: onr:"swepub:oai:DiVA.org:hh-52469" > Evolving intelligen...
Fältnamn | Indikatorer | Metadata |
---|---|---|
000 | 04362nam a2200469 4500 | |
001 | oai:DiVA.org:hh-52469 | |
003 | SwePub | |
008 | 240124s2024 | |||||||||||000 ||eng| | |
020 | a 9789189587311q electronic | |
020 | a 9789189587328q print | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-524692 URI |
040 | a (SwePub)hh | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a vet2 swepub-contenttype |
072 | 7 | a dok2 swepub-publicationtype |
100 | 1 | a Altarabichi, Mohammed Ghaith,d 1981-u Högskolan i Halmstad,Akademin för informationsteknologi4 aut0 (Swepub:hh)mohalt |
245 | 1 0 | a Evolving intelligence :b Overcoming challenges for Evolutionary Deep Learning |
264 | 1 | a Halmstad :b Halmstad University Press,c 2024 |
300 | a 32 s. | |
338 | a electronic2 rdacarrier | |
490 | 0 | a Halmstad University Dissertations ;v 109 |
520 | a 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. | |
650 | 7 | a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Datorsystem0 (SwePub)202062 hsv//swe |
650 | 7 | a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Computer Systems0 (SwePub)202062 hsv//eng |
650 | 7 | a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Signalbehandling0 (SwePub)202052 hsv//swe |
650 | 7 | a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Signal Processing0 (SwePub)202052 hsv//eng |
653 | a neural networks | |
653 | a evolutionary deep learning | |
653 | a evolutionary machine learning | |
653 | a feature selection | |
653 | a hyperparameter optimization | |
653 | a evolutionary computation | |
653 | a particle swarm optimization | |
653 | a genetic algorithm | |
700 | 1 | a Nowaczyk, Sławomir,c Professor,d 1978-u Högskolan i Halmstad,Akademin för informationsteknologi4 ths0 (Swepub:hh)slanow |
700 | 1 | a Pashami, Sepideh,c Associate Professor,d 1985-u Högskolan i Halmstad,Akademin för informationsteknologi4 ths0 (Swepub:hh)seppas |
700 | 1 | a Sheikholharam Mashhadi, Peyman,c Senior Lecturer,d 1982-u Högskolan i Halmstad,Akademin för informationsteknologi4 ths0 (Swepub:hh)peymas |
700 | 1 | a Lavesson, Niklas,c Professor,d 1985-u Blekinge Institute of Technology, Karlskrona, Sweden4 opn0 (Swepub:hh)seppas |
710 | 2 | a Högskolan i Halmstadb Akademin för informationsteknologi4 org |
856 | 4 | u https://hh.diva-portal.org/smash/get/diva2:1831077/FULLTEXT02.pdfx primaryx Raw objecty fulltext |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-52469 |
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
Kopiera och spara länken för att återkomma till aktuell vy