43 citations · 98 across the 13 of their papers we have counts for
15 papers
Sampling on Random Subspaces under Limited Data in the Context of Exploratory Landscape Analysis
Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1
Classical space-filling designs often fail to provide reliable statistical results for Exploratory Landscape Analysis (ELA) when only limited evaluation budgets are available, as c…
Improving Evaluation of Recombination-based Cartesian Genetic Programming
Duy Long Tran, Anja Jankovic, Marie Anastacio +2
Cartesian Genetic Programming has traditionally been using mutation as its main and often sole genetic operator to drive evolutionary search. Despite advancements in recent years,…
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1
Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsit…
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
Roman Kalkreuth, Fabricio Olivetti de França, Julian Dierkes +4
Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, i…
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten +6
The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires b…
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Ana Kostovska, Anja Jankovic, Diederick Vermetten +3
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can…
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +2
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is hig…
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
Carolin Benjamins, Anja Jankovic, Elena Raponi +3
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problem…
PI is back! Switching Acquisition Functions in Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +4
Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquis…
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features
Ana Kostovska, Anja Jankovic, Diederick Vermetten +4
Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform…
Trajectory-based Algorithm Selection with Warm-starting
Anja Jankovic, Diederick Vermetten, Ana Kostovska +3
Landscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization…
Personalizing Performance Regression Models to Black-Box Optimization Problems
Tome Eftimov, Anja Jankovic, Gorjan Popovski +2
Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configurat…
The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
Anja Jankovic, Gorjan Popovski, Tome Eftimov +1
Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a…
Towards Feature-Based Performance Regression Using Trajectory Data
Anja Jankovic, Tome Eftimov, Carola Doerr
Black-box optimization is a very active area of research, with many new algorithms being developed every year. This variety is needed, on the one hand, since different algorithms a…
Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
Anja Jankovic, Carola Doerr
Automated algorithm selection promises to support the user in the decisive task of selecting a most suitable algorithm for a given problem. A common component of these machine-trai…