Keywords: feature selection, nature-inspired algorithms, swarm intelligence, optimizationPublished in DKUM: 05.04.2024; Views: 218; Downloads: 21 Full text (1,14 MB)
Keywords: predictive maintenance, autoencoder, anomaly detection, nature-inspired algorithms, optimization, high-performance computing, unsupervised learningPublished in DKUM: 26.01.2024; Views: 447; Downloads: 0
Abstract: This master thesis is focusing on the utilization of nature-inspired algorithms for hyperparameter optimization, how they work and how to use them. We present some existing methods for hyperparameter optimization as well as propose a novel method that is based on six different nature-inspired algorithms: Firefly algorithm, Grey Wolf Optimizer, Particle Swarm Optimization, Genetic algorithm, Differential Evolution, and Hybrid Bat algorithm. We also show the optimization results (set of hyperparameters) for each algorithm and we present the plots of the accuracy for each combination and handpicked one. In discussion of the results, we provide the answers on our research questions as well as propose ideas for future work.Keywords: artificial intelligence, artificial neural networks, machine learning, nature-inspired algorithms, evolutionary algorithmsPublished in DKUM: 09.12.2019; Views: 2232; Downloads: 122 Full text (969,13 KB)
