EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE - Baljinder Kaur; Manik Rakhra; Nonita Sharma; Monika Mangla (Proceedings on Engineering Sciences)
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.