Optimisation Problem
Optimization problems, aiming to find the best solution among many possibilities, are central to numerous scientific and engineering disciplines. Current research emphasizes developing efficient algorithms, including those leveraging quantum computing, machine learning (e.g., neural networks, Bayesian optimization), and evolutionary computation (e.g., genetic algorithms), to tackle increasingly complex problems, such as those with multiple objectives, dynamic environments, or constraints. These advancements are improving the ability to solve real-world problems across diverse fields, from logistics and resource allocation to materials science and drug discovery, by providing better solutions faster and more efficiently.
Papers
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation
Adel Nikfarjam, Amirhossein Moosavi, Aneta Neumann, Frank Neumann
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
Adel Nikfarjam, Aneta Neumann, Jakob Bossek, Frank Neumann