EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE
This paper presents a novel approach for hyperparameter optimization for the MobileNetV2 architecture using a genetic algorithm. The proposed approach aims to automate the hyperparameter tuning leading to performance enhancement. This automated approach conserves the computational overheads involved...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2025-03-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v7-n1/61.pdf |
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| author | Baljinder Kaur Manik Rakhra Nonita Sharma Monika Mangla |
| author_facet | Baljinder Kaur Manik Rakhra Nonita Sharma Monika Mangla |
| author_sort | Baljinder Kaur |
| collection | DOAJ |
| description | This paper presents a novel approach for hyperparameter optimization for the MobileNetV2 architecture using a genetic algorithm. The proposed approach aims to automate the hyperparameter tuning leading to performance enhancement. This automated approach conserves the computational overheads involved in traditional hyperparameter tuning methods. This automated method for hyperparameter tuning is the result of significant advancement in the domain of deep learning models and eventually offers a scalable and efficient solution for developing high-performance mobile applications. This understanding will surely aid authors to devise an intriguing solution to address the involved challenges. Authors have provided 2-step solution where first part proposes a novel genetic algorithm based hyperparameter optimization followed by creation of a lightweight deep learning architecture, the second step of the solution. Further, the authors also aim to devise a mobile application that widens the scope of real-life application of the case study. Here, authors have undertaken the case study of poultry disease identification to evaluate the effectiveness and efficiency of proposed solution. |
| format | Article |
| id | doaj-art-c7589afe3f9546a198c0f4e9a569bd30 |
| institution | DOAJ |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-c7589afe3f9546a198c0f4e9a569bd302025-08-20T02:56:55ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-03-017158760010.24874/PES07.01D.015EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTUREBaljinder Kaur 0https://orcid.org/0000-0001-9922-5258Manik Rakhra 1https://orcid.org/0000-0003-1680-6992Nonita Sharma 2https://orcid.org/0000-0002-3132-3748Monika Mangla 3https://orcid.org/0000-0002-1752-7226School of Computer Science, Lovely Professional Univeristy, Phagwara, India School of Computer Science, Lovely Professional Univeristy, Phagwara, India Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi India Department of Information Technology, Dwarkadas J. Sanghvi College of Engineering, Mumbai India This paper presents a novel approach for hyperparameter optimization for the MobileNetV2 architecture using a genetic algorithm. The proposed approach aims to automate the hyperparameter tuning leading to performance enhancement. This automated approach conserves the computational overheads involved in traditional hyperparameter tuning methods. This automated method for hyperparameter tuning is the result of significant advancement in the domain of deep learning models and eventually offers a scalable and efficient solution for developing high-performance mobile applications. This understanding will surely aid authors to devise an intriguing solution to address the involved challenges. Authors have provided 2-step solution where first part proposes a novel genetic algorithm based hyperparameter optimization followed by creation of a lightweight deep learning architecture, the second step of the solution. Further, the authors also aim to devise a mobile application that widens the scope of real-life application of the case study. Here, authors have undertaken the case study of poultry disease identification to evaluate the effectiveness and efficiency of proposed solution.https://pesjournal.net/journal/v7-n1/61.pdfpredictive modelingremote monitoringdecision support systemmobilenet2hyperparameter optimizationgenetic algorithm |
| spellingShingle | Baljinder Kaur Manik Rakhra Nonita Sharma Monika Mangla EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE Proceedings on Engineering Sciences predictive modeling remote monitoring decision support system mobilenet2 hyperparameter optimization genetic algorithm |
| title | EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE |
| title_full | EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE |
| title_fullStr | EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE |
| title_full_unstemmed | EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE |
| title_short | EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE |
| title_sort | employing genetic algorithm inspired hyperparameter optimization in mobile net v2 architecture |
| topic | predictive modeling remote monitoring decision support system mobilenet2 hyperparameter optimization genetic algorithm |
| url | https://pesjournal.net/journal/v7-n1/61.pdf |
| work_keys_str_mv | AT baljinderkaur employinggeneticalgorithminspiredhyperparameteroptimizationinmobilenetv2architecture AT manikrakhra employinggeneticalgorithminspiredhyperparameteroptimizationinmobilenetv2architecture AT nonitasharma employinggeneticalgorithminspiredhyperparameteroptimizationinmobilenetv2architecture AT monikamangla employinggeneticalgorithminspiredhyperparameteroptimizationinmobilenetv2architecture |