8 citations · 18 across the 16 of their papers we have counts for
16 papers
Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Maryam Gholami Shiri, Eva Tuba, Sašo Džeroski +2
Benchmarking deep learning (DL) models for multi-label classification (MLC) of remote sensing images (RSI) typically yields rankings that do not generalize beyond the evaluated dat…
Quantifying the Impact of Modules and Their Interactions in the PSO-X Framework
Christian L. Camacho-Villalón, Ana Nikolikj, Katharina Dost +3
The PSO-X framework incorporates dozens of modules that have been proposed for solving single-objective continuous optimization problems using particle swarm optimization. While mo…
Longest Filled Common Subsequence for Song Identification from Degraded Audio via Construct--Merge--Solve--Adapt Optimization
Marko Djukanovic, Christian Blum, Aleksandar Kartelj +2
This paper addresses the Longest Filled Common Subsequence (LFCS) problem, a challenging NP-hard problem with applications in bioinformatics, including gene mutation prediction and…
Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes
Ana Nikolikj, Mario Andrés Muñoz, Eva Tuba +1
This paper leverages the recently introduced concept of algorithm footprints to investigate the interplay between algorithm configurations and problem characteristics. Performance…
Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance
Ana Nikolikj, Gabriela Ochoa, Tome Eftimov
We present an analysis of landscape features for predicting the performance of multi-objective combinatorial optimization algorithms. We consider features from the recently propose…
A Learning Search Algorithm for the Restricted Longest Common Subsequence Problem
Marko Djukanović, Jaume Reixach, Ana Nikolikj +3
This paper addresses the Restricted Longest Common Subsequence (RLCS) problem, an extension of the well-known Longest Common Subsequence (LCS) problem. This problem has significant…
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj +3
Dynamic Algorithm Configuration (DAC) addresses the challenge of dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on i…
A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization
Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin +3
This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-ob…
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
Ana Nikolikj, Ana Kostovska, Gjorgjina Cenikj +2
This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem…
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
Ana Nikolikj, Ana Kostovska, Diederick Vermetten +2
This study explores the influence of modules on the performance of modular optimization frameworks for continuous single-objective black-box optimization. There is an extensive var…
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…
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
Ana Nikolikj, Sašo Džeroski, Mario Andrés Muñoz +3
In black-box optimization, it is essential to understand why an algorithm instance works on a set of problem instances while failing on others and provide explanations of its behav…
Assessing the Generalizability of a Performance Predictive Model
Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova +6
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive…
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction
Ana Nikolikj, Michal Pluháček, Carola Doerr +2
Leave-one-problem-out (LOPO) performance prediction requires machine learning (ML) models to extrapolate algorithms' performance from a set of training problems to a previously uns…
RF+clust for Leave-One-Problem-Out Performance Prediction
Ana Nikolikj, Carola Doerr, Tome Eftimov
Per-instance automated algorithm configuration and selection are gaining significant moments in evolutionary computation in recent years. Two crucial, sometimes implicit, ingredien…
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…