82 citations · 96 across the 7 of their papers we have counts for
5 papers · 1 filter
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj +6
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available compu…
Extensible Logging and Empirical Attainment Function for IOHexperimenter
Johann Dreo, Manuel López-Ibáñez
In order to allow for large-scale, landscape-aware, per-instance algorithm selection, a benchmarking platform software is key. IOHexperimenter provides a large set of synthetic pro…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
Unified Framework for the Adaptive Operator Selection of Discrete Parameters
Mudita Sharma, Manuel Lopez-Ibanez, Dimitar Kazakov
We conduct an exhaustive survey of adaptive selection of operators (AOS) in Evolutionary Algorithms (EAs). We simplified the AOS structure by adding more components to the framewor…
Deep Reinforcement Learning Based Parameter Control in Differential Evolution
Mudita Sharma, Alexandros Komninos, Manuel Lopez Ibanez +1
Adaptive Operator Selection (AOS) is an approach that controls discrete parameters of an Evolutionary Algorithm (EA) during the run. In this paper, we propose an AOS method based o…