2 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
On the Structural (Dis)Agreement of Landscape Representations in Black-Box Optimization
Sara Gjorgjieva, Eva Tuba, Barbara Koroušić Seljak +2
Landscape feature representations play a central role in automated algorithm selection and meta-learning for black-box optimization, yet little is known about how different represe…
Similarity-based Portfolio Construction for Black-box Optimization
Catalin-Viorel Dinu, Diederick Vermetten, Carola Doerr
In black-box optimization, a central question is which algorithm to use to solve a given, previously unseen, problem. Selecting a single algorithm, however, entails inherent risks:…
When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection
Denis Antipov, Carola Doerr
Online algorithm selection (OAS) aims to adapt the optimization process to changes in the fitness landscape and is expected to outperform any single algorithm from a given portfoli…
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…
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…
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…