Explaining Differential Evolution Performance Through Problem Landscape Characteristics. - Ana Nikolikj; Ryan Dieter Lang; Peter Korosec; Tome Eftimov (BIOMA, 2022)

Identifying minimal set of Exploratory Landscape Analysis features for reliable algorithm performance prediction. - Ana Nikolikj; Risto Trajanov; Gjorgjina Cenikj; Peter Korosec; Tome Eftimov (CEC, 2022)

Improving Nevergrad's Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration. - Risto Trajanov; Ana Nikolikj; Gjorgjina Cenikj; Fabien Teytaud; Mathurin Videau; Olivier Teytaud; Tome Eftimov; Manuel López-Ibáñez 001; Carola Doerr (PPSN (1), 2022)

PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization. - Ana Kostovska; Gjorgjina Cenikj; Diederick Vermetten; Anja Jankovic 001; Ana Nikolikj; Urban Skvorc; Peter Korosec; Carola Doerr; Tome Eftimov (AutoML, 2023)

Sensitivity Analysis of RF+clust for Leave-One-Problem-Out Performance Prediction. - Ana Nikolikj; Michal Pluhacek; Carola Doerr; Peter Korosec; Tome Eftimov (CEC, 2023)

RF+clust for Leave-One-Problem-Out Performance Prediction. - Ana Nikolikj; Carola Doerr; Tome Eftimov (EvoApplications@EvoStar, 2023)

Assessing the Generalizability of a Performance Predictive Model. - Ana Nikolikj; Gjorgjina Cenikj; Gordana Ispirova; Diederick Vermetten; Ryan Dieter Lang; Andries Petrus Engelbrecht; Carola Doerr; Peter Korosec; Tome Eftimov (GECCO Companion, 2023)

Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances. - Ana Nikolikj; Saso Dzeroski; Mario Andrés Muñoz; Carola Doerr; Peter Korosec; Tome Eftimov (GECCO, 2023)

Generalization Ability of Feature-Based Performance Prediction Models: A Statistical Analysis Across Benchmarks. - Ana Nikolikj; Ana Kostovska; Gjorgjina Cenikj; Carola Doerr; Tome Eftimov (CEC, 2024)

Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks. - Ana Nikolikj; Ana Kostovska; Diederick Vermetten; Carola Doerr; Tome Eftimov (CEC, 2024)

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. - Carolin Benjamins; Gjorgjina Cenikj; Ana Nikolikj; Aditya Mohan; Tome Eftimov; Marius Lindauer (GECCO Companion, 2024)

Comparing Solvability Patterns of Algorithms across Diverse Problem Landscapes. - Ana Nikolikj; Tome Eftimov (GECCO Companion, 2024)

A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization. - Gjorgjina Cenikj; Ana Nikolikj; Gasper Petelin; Niki van Stein; Carola Doerr; Tome Eftimov (CoRR, 2024)

Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes. - Ana Nikolikj; Mario Andrés Muñoz; Eva Tuba; Tome Eftimov (CEC, 2025)

Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance. - Ana Nikolikj; Gabriela Ochoa; Tome Eftimov (GECCO, 2025)

A learning search algorithm for the Restricted Longest Common Subsequence problem. - Marko Djukanovic; Jaume Reixach; Ana Nikolikj; Tome Eftimov; Aleksandar Kartelj; Christian Blum 001 (Expert Syst. Appl., 2025)

Exploring module interactions in modular CMA-ES across problem classes. - Ana Nikolikj; Tome Eftimov (Swarm Evol. Comput., 2025)

Benchmarking footprints of continuous black-box optimization algorithms: Explainable insights into algorithm success and failure. - Ana Nikolikj; Mario Andrés Muñoz; Tome Eftimov (Swarm Evol. Comput., 2025)