Publications

A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks

Transactions on Emerging Telecommunications Technologies Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource‐constrained and very heterogeneous, t...

A New Baseline for Automated Hyper-Parameter Optimization

LOD 2019: Machine Learning, Optimization, and Data Science Finding the optimal hyper-parameters values for a given problem is essential for most machine learning algorithms. In this paper, we propose a novel hyper-parameter optimization algorithm...

Evaluating Population Based Training on Small Datasets

NIK2019 – 2019: Norsk Informatikkonferanse Recently, there has been an increased interest in using artificial neural networks in the severely resource-constrained devices found in Internet-of-Things networks, in order to perform actions learned f...

A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost

SpliTech2019 – 4th International Conference on Smart and Sustainable Technologies The Internet of Things (IoT) will connect more than 50 billion heterogenous devices around the world by 2020. As an Ultra Dense Network (UDN), which needs high resources t...

Using automatic programming to design improved variants of differential evolution

2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES) To automatically design improvements of stochastic numerical optimization algorithms is challenging due to the high computation time required to ensure sufficiently rigorous...

Improving competitive differential evolution using automatic programming

2017 4th International Conference on Systems and Informatics (ICSAI) In this paper, we automatically improve the competitive differential evolution algorithm through automatic programming. The improved algorithm outperforms the original for over 73%...

Improving differential evolution using inductive programming (Master thesis)

Abstract Differential Evolution (DE) has emerged as one of the most powerful and versatile global numerical optimizers for non-differentiable, multimodal problems. The popularity of DE has led to extensive work on improving the algorithm, and significant...