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∙ 06/30/2023

Comparing Algorithm Selection Approaches on Black-Box Optimization Problems

Performance complementarity of solvers available to tackle black-box opt...

0 Ana Kostovska, et al. ∙

∙ 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...

0 Gjorgjina Cenikj, et al. ∙

∙ 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...

0 Ana Nikolikj, et al. ∙

∙ 05/31/2023

Assessing the Generalizability of a Performance Predictive Model

A key component of automated algorithm selection and configuration, whic...

0 Ana Nikolikj, et al. ∙

∙ 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...

0 Ana Nikolikj, et al. ∙

∙ 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...

0 Gordana Ispirova, et al. ∙

∙ 01/24/2023

Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms

Empirical data plays an important role in evolutionary computation resea...

0 Ana Kostovska, et al. ∙

∙ 01/23/2023

RF+clust for Leave-One-Problem-Out Performance Prediction

Per-instance automated algorithm configuration and selection are gaining...

0 Ana Nikolikj, et al. ∙

∙ 11/21/2022

Explainable Model-specific Algorithm Selection for Multi-Label Classification

Multi-label classification (MLC) is an ML task of predictive modeling in...

0 Ana Kostovska, et al. ∙

∙ 09/09/2022

Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

Algorithm selection wizards are effective and versatile tools that autom...

0 Risto Trajanov, et al. ∙

∙ 04/25/2022

SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison

Fair algorithm evaluation is conditioned on the existence of high-qualit...

0 Gjorgjina Cenikj, et al. ∙

∙ 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...

4 Ana Kostovska, et al. ∙

∙ 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...

14 Ana Kostovska, et al. ∙

∙ 04/13/2022

Trajectory-based Algorithm Selection with Warm-starting

Landscape-aware algorithm selection approaches have so far mostly been r...

2 Anja Jankovic, et al. ∙

∙ 03/22/2022

Explainable Landscape Analysis in Automated Algorithm Performance Prediction

Predicting the performance of an optimization algorithm on a new problem...

0 Risto Trajanov, et al. ∙

∙ 10/22/2021

Explainable Landscape-Aware Optimization Performance Prediction

Efficient solving of an unseen optimization problem is related to approp...

0 Risto Trajanov, et al. ∙

∙ 10/05/2021

FoodChem: A food-chemical relation extraction model

In this paper, we present FoodChem, a new Relation Extraction (RE) model...

0 Gjorgjina Cenikj, et al. ∙

∙ 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 ...

0 Urban Škvorc, et al. ∙

∙ 04/24/2021

OPTION: OPTImization Algorithm Benchmarking ONtology

Many platforms for benchmarking optimization algorithms offer users the ...

0 Ana Kostovska, et al. ∙

∙ 04/22/2021

Personalizing Performance Regression Models to Black-Box Optimization Problems

Accurately predicting the performance of different optimization algorith...

0 Tome Eftimov, et al. ∙

∙ 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...

6 Anja Jankovic, et al. ∙

∙ 02/10/2021

Towards Feature-Based Performance Regression Using Trajectory Data

Black-box optimization is a very active area of research, with many new ...

0 Anja Jankovic, et al. ∙

∙ 12/01/2020

On Statistical Analysis of MOEAs with Multiple Performance Indicators

Assessing the empirical performance of Multi-Objective Evolutionary Algo...

8 Hao Wang, et al. ∙

∙ 09/30/2020

Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes

Automated per-instance algorithm selection and configuration have shown ...

0 Tome Eftimov, et al. ∙

∙ 07/07/2020

Benchmarking in Optimization: Best Practice and Open Issues

This survey compiles ideas and recommendations from more than a dozen re...

11 Thomas Bartz-Beielstein, et al. ∙

∙ 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...

0 Khushbu Agarwal, et al. ∙

∙ 05/27/2019

A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic

The United States is in the midst of an opioid epidemic with recent esti...

0 Maulik R. Kamdar, et al. ∙

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