2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation
Tai Nguyen, Fei Liu, Phong Le +2
Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms can be excessive. We consid…
Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning
Tai Nguyen, Phong Le, Carola Doerr +1
While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remain…
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…
Multi-parameter Control for the -GA on OneMax via Deep Reinforcement Learning
Tai Nguyen, Phong Le, Carola Doerr +1
It is well known that evolutionary algorithms can benefit from dynamic choices of the key parameters that control their behavior, to adjust their search strategy to the different s…
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…