research
∙
06/30/2023
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Performance complementarity of solvers available to tackle black-box opt...
share
research
∙
06/08/2023
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
The application of machine learning (ML) models to the analysis of optim...
research
∙
06/01/2023
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
In black-box optimization, it is essential to understand why an algorith...
research
∙
05/31/2023
Assessing the Generalizability of a Performance Predictive Model
A key component of automated algorithm selection and configuration, whic...
research
∙
05/30/2023
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction
Leave-one-problem-out (LOPO) performance prediction requires machine lea...
research
∙
02/02/2023
Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets
Although recipe data are very easy to come by nowadays, it is really har...
research
∙
01/24/2023
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
Empirical data plays an important role in evolutionary computation resea...
research
∙
01/23/2023
RF+clust for Leave-One-Problem-Out Performance Prediction
Per-instance automated algorithm configuration and selection are gaining...
research
∙
11/21/2022
Explainable Model-specific Algorithm Selection for Multi-Label Classification
Multi-label classification (MLC) is an ML task of predictive modeling in...
research
∙
09/09/2022
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Algorithm selection wizards are effective and versatile tools that autom...
research
∙
04/25/2022
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison
Fair algorithm evaluation is conditioned on the existence of high-qualit...
research
∙
04/20/2022
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features
Per-instance algorithm selection seeks to recommend, for a given problem...
research
∙
04/15/2022
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
Selecting the most suitable algorithm and determining its hyperparameter...
research
∙
04/13/2022
Trajectory-based Algorithm Selection with Warm-starting
Landscape-aware algorithm selection approaches have so far mostly been r...
research
∙
03/22/2022
Explainable Landscape Analysis in Automated Algorithm Performance Prediction
Predicting the performance of an optimization algorithm on a new problem...
research
∙
10/22/2021
Explainable Landscape-Aware Optimization Performance Prediction
Efficient solving of an unseen optimization problem is related to approp...
research
∙
10/05/2021
FoodChem: A food-chemical relation extraction model
In this paper, we present FoodChem, a new Relation Extraction (RE) model...
research
∙
04/27/2021
A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems
When designing a benchmark problem set, it is important to create a set ...
research
∙
04/24/2021
OPTION: OPTImization Algorithm Benchmarking ONtology
Many platforms for benchmarking optimization algorithms offer users the ...
research
∙
04/22/2021
Personalizing Performance Regression Models to Black-Box Optimization Problems
Accurately predicting the performance of different optimization algorith...
research
∙
04/19/2021
The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
Automated algorithm selection and configuration methods that build on ex...
research
∙
02/10/2021
Towards Feature-Based Performance Regression Using Trajectory Data
Black-box optimization is a very active area of research, with many new ...
research
∙
12/01/2020
On Statistical Analysis of MOEAs with Multiple Performance Indicators
Assessing the empirical performance of Multi-Objective Evolutionary Algo...
research
∙
09/30/2020
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes
Automated per-instance algorithm selection and configuration have shown ...
research
∙
07/07/2020
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen re...
research
∙
07/19/2019
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Representation learning methods that transform encoded data (e.g., diagn...
research
∙
05/27/2019
