7 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…
Graph Instance Landscapes: When Structural Similarity Does (Not) Reflect Shortest-Path Performance
Maryam Gholami Shiri, Ivana Krminac, Marko DjukanoviÄ +3
Benchmarking shortest-path algorithms is commonly based on aggregate performance over heterogeneous graph sets, which limits insight into how different search paradigms react to in…
Evaluating Real-World Generalizability of Algorithm Selection Models
Gjorgjina Cenikj, Jakub Kudela, Eva Tuba +1
Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable problem characteristics and h…
Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization
Sara Gjorgjieva, Eva Tuba, Tome Eftimov
In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but how to determine when suffic…
On the Structural (Dis)Agreement of Landscape Representations in Black-Box Optimization
Sara Gjorgjieva, Eva Tuba, Barbara KorouÅ¡iÄ Seljak +2
Landscape feature representations play a central role in automated algorithm selection and meta-learning for black-box optimization, yet little is known about how different represe…
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…