21 citations · 40 across the 15 of their papers we have counts for
16 papers
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…
Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
Denica Kjorvezir, Marko Djukanović, Ana Gjorgjevikj +2
Evaluating large language models (LLMs) across comprehensive benchmarks is expensive and time-consuming. We propose a graph-based prompt selection framework that models each benchm…
ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Gjorgjina Cenikj, Gašper Petelin, Tome Eftimov
Understanding the behavior of numerical metaheuristic optimization algorithms is critical for advancing their development and application. Traditional visualization techniques, suc…
Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
Gjorgjina Cenikj, Gašper Petelin, Tome Eftimov
The field of numerical optimization has recently seen a surge in the development of "novel" metaheuristic algorithms, inspired by metaphors derived from natural or human-made proce…
The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong
Gašper Petelin, Gjorgjina Cenikj
Algorithm selection, aiming to identify the best algorithm for a given problem, plays a pivotal role in continuous black-box optimization. A common approach involves representing o…
Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization?
Gjorgjina Cenikj, Gašper Petelin, Moritz Seiler +2
%% Text of abstract The process of identifying the most suitable optimization algorithm for a specific problem, referred to as algorithm selection (AS), entails training models tha…
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…
TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
Gjorgjina Cenikj, Gašper Petelin, Tome Eftimov
We propose a representation of optimization problem instances using a transformer-based neural network architecture trained for the task of problem classification of the 24 problem…
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…
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
Gjorgjina Cenikj, Gašper Petelin, Carola Doerr +2
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These feat…
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…
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…
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison
Gjorgjina Cenikj, Ryan Dieter Lang, Andries Petrus Engelbrecht +3
Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets that are non-redundant and are representative of typical optimization scenarios. In thi…
FoodChem: A food-chemical relation extraction model
Gjorgjina Cenikj, Barbara Koroušić Seljak, Tome Eftimov
In this paper, we present FoodChem, a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities, based on textual information provide…