4 papers
Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the (1+(,))-GA
Tai Nguyen, Phong Le, André Biedenkapp +2
Dynamic Algorithm Configuration (DAC) studies the efficient identification of control policies for parameterized optimization algorithms. Numerous studies leverage Reinforcement Le…
Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
Theresa Eimer, Lennart Schäpermeier, André Biedenkapp +15
Empirical research on meta-algorithmics, such as algorithm selection, configuration, and scheduling, often relies on extensive and thus computationally expensive experiments. With…
A Llama walks into the 'Bar': Efficient Supervised Fine-Tuning for Legal Reasoning in the Multi-state Bar Exam
Rean Fernandes, André Biedenkapp, Frank Hutter +1
Legal reasoning tasks present unique challenges for large language models (LLMs) due to the complexity of domain-specific knowledge and reasoning processes. This paper investigates…
On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(,))-GA
Tai Nguyen, Phong Le, André Biedenkapp +2
Dynamic Algorithm Configuration (DAC) has garnered significant attention in recent years, particularly in the prevalence of machine learning and deep learning algorithms. Numerous…