Carola Doerr

collaborators

3 papers

cs.NE2025

Enhancing Parameter Control Policies with State Information

Gianluca Covini, Denis Antipov, Carola Doerr

Parameter control and dynamic algorithm configuration study how to dynamically choose suitable configurations of a parametrized algorithm during the optimization process. Despite b…

cs.LG2025

Multi-parameter Control for the $(1 + (λ, λ))$ -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…

cs.LG2025

On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+( $λ$ , $λ$ ))-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…

Enhancing Parameter Control Policies with State Information

Multi-parameter Control for the (1+(λ,λ))-GA on OneMax via Deep Reinforcement Learning

On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GA

Multi-parameter Control for the $(1 + (λ, λ))$ -GA on OneMax via Deep Reinforcement Learning

On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+( $λ$ , $λ$ ))-GA