Related publications:

Journal articles:

Eftimov, T., Paudel. B., Popovski, G., and Kocev, D. (2021). A Framework for Evaluating Personalized Ranking Systems by Fusing Different Evaluation Measures. Big Data Research.

Eftimov, T., and Korošec, P. (2021). Deep Statistical Comparison for Multi-Objective Stochastic Optimization Algorithms. Swarm and Evolutionary Computation.

Eftimov, T., Popovski, G., Petković, M., Koroušić Seljak, B., and Kocev, D. (2020). COVID-19 pandemic changes the food consumption patterns. Trends in Food Science & Technology.

Korošec, P., and Eftimov, T. (2020). Insights into Exploration and Exploitation Power of Optimization Algorithm Using DSCTool. Mathematics, 8, 1474.

Korošec, P., and Eftimov, T. (2020). Multi-Objective Optimization Benchmarking Using DSCTool. Mathematics, 8, 839.

Škvorc, U., Eftimov, T., and Korošec, P. (2020). Understanding the problem space in single-objective numerical optimization using exploratory landscape analysis. Applied Soft Computing 106138.

Simjanoska, M., Kochev, S., Tanevski, J., Bogdanova, A. M., Papa, G., and Eftimov, T. (2020). Multi-level information fusion for learning a blood pressure predictive model using sensor data. Information Fusion.

Eftimov, T., Petelin, G., and Korošec, P. (2019). DSCTool: A web-service-based framework for statistical comparison of stochastic optimization algorithms. Applied Soft Computing, 105977.

Eftimov, T., and Korošec, P. (2019). Identifying practical significance through statistical comparison of meta-heuristic stochastic optimization algorithms. Applied Soft Computing, 105862.

Conference articles:

Eftimov, T., Jankovic, A., Popovski, G., Doerr, C., and Korošec, P. (2021, July). Personalizing Performance Regression Models to Black-Box Optimization Problems. In the Genetic and Evolutionary Computation Conference (GECCO 2021)

Škvorc, U., Eftimov, T., and Korošec, P. (2021, July). A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems. In the Genetic and Evolutionary Computation Conference (GECCO 2021)

Kostovska, A., Vermetten, D., Doerr, C., Džeroski, S., Panov, P.,, and Eftimov, T. (2021, July). OPTION: OPTmization Algorithm Benchmarking ONtology. In the Genetic and Evolutionary Computation Conference (GECCO 2021)

Jankovic, A., Eftimov, T., and Doerr, C. (2021, Aptil). Towards Feature-Based Performance Regression Using Trajectory Data. In: Castillo P.A., Jiménez Laredo J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science, vol 12694. Springer, Cham.

Wang, H., Hernández, C., and Eftimov, T. (2021, March). On Statistical Analysis of MOEAs with Multiple Performance Indicators. In: Ishibuchi H. et al. (eds) Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science, vol 12654. Springer, Cham

Eftimov, T., Popovski, G., Renau, Q, Korošec, P., and Doerr, C. (2020, December). Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes, In the IEEE Series Symposium on Computation Intelligence 2020 (IEEE SSCI 2020)

Tuba, E., Korošec, P., and Eftimov, T. (2020, October). In-depth Insights into Swarm Intelligence Algorithms, In the Modelling and Development of Intelligent Systems 2020 (MDIS 2020)

Škvorc, U., Eftimov, T., and Korošec, P. (2020, July). Using Exploratory Landscape Analysis to Visualize Single-objective Problems, In the Genetic and Evolutionary Computation Conference (GECCO 2020) (Hot-Off-the-Press Track)

Eftimov, T., Petelin, G., Hribar, R., Popovski, G., Škvorc, U., and Korošec, P. (2020, July). PerformViz: A Machine Learning Approach to Visualize and Understand the Performance of Single-objective Optimization Algorithms, In the Genetic and Evolutionary Computation Conference (GECCO 2020) (Open Optimmization Competition 2020)

Eftimov, T., Hribar, R., Škvorc, U., Popovski, G., Petelin, G., and Korošec, P. (2020, July). Deep Statistics: More Robust Performance Statistics for Single-objective Optimization Benchmarking, In the Genetic and Evolutionary Computation Conference (GECCO 2020) (Open Optimmization Competition 2020)

Eftimov, T., and Korošec, P. (2020, July). Is the statistical significance between stochastic optimization algorithms' performances also significant in practice?, In the Genetic and Evolutionary Computation Conference (GECCO 2020) (Hot-Off-the-Press Track)

Eftimov, T., Popovski, G., Kocev, D., and Korošec, P. (2020, July). Performance2vec: A step further in explainable stochastic optimization algorithm perforamnce, In the Genetic and Evolutionary Computation Conference (GECCO 2020)

Paudel, B., Kocev, D., and Eftimov, T. (2019, December). Mix and Rank: A Framework for Benchmarking Recommender Systems, In the Third IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2019) at IEEE BigData 2019

Chapter of book:

Simjanoska, M., Papa, G., Koroušič Seljak, B., and Eftimov, T. (2020). ECGpp: A Framework for Selecting the Pre-processing Parameters of ECG Signals Used for Blood Pressure Classification. In: Roque A. et al. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2019. Communications in Computer and Information Science, vol 1211. Springer, Cham

Conference articles:

Škvorc, U., Eftimov, T., and Korošec, P. (2019). CEC real-parameter optimization competitions: Progress from 2013 to 2018. In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 3127-3134). IEEE.

Eftimov, T., and Kocev, D. (2019). Performance Measures Fusion for Experimental Comparison of Methods for Multi-label Classification. In AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering.

Simjanoska, M.; Papa, G.; Seljak, B. and Eftimov, T. (2019). Comparing Different Settings of Parameters Needed for Pre-processing of ECG Signals used for Blood Pressure Classification.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-353-7, pages 62-72. DOI: 10.5220/0007390100620072

Eftimov, T., and Korošec, P. (2018, July). The impact of statistics for benchmarking in evolutionary computation research. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1329-1336). ACM.

Eftimov, T., Korošec, P., and Koroušić Seljak, B. (2018, July). Deep statistical comparison of meta-heuristic stochastic optimization algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 15-16). ACM. (Hot-Off-the-Press paper)

Eftimov, T., Korošec, P., and Koroušić Seljak, B. (2018, May). Data-Driven Preference-Based Deep Statistical Ranking for Comparing Multi-objective Optimization Algorithms. In International Conference on Bioinspired Methods and Their Applications (pp. 138-150). Springer, Cham.

Eftimov, T., Korošec, P., and Koroušić Seljak, B. (2017, November). Comparing multi-objective optimization algorithms using an ensemble of quality indicators with deep statistical comparison approach. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE.

Eftimov, T., Korošec, P., and Koroušić Seljak, B. (2017, September). Deep statistical comparison applied on quality indicators to compare multi-objective stochastic optimization algorithms. In International Workshop on Machine Learning, Optimization, and Big Data (pp. 76-87). Springer, Cham.

Eftimov, T.; Korošec, P. and Koroušić Seljak, B. (2017). The Behavior of Deep Statistical Comparison Approach for Different Criteria of Comparing Distributions.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 73-82. DOI: 10.5220/0006499900730082.

Eftimov, T., Korošec, P., and Koroušić Seljak, B. (2016). Disadvantages of statistical comparison of stochastic optimization algorithms. Proceedings of the Bioinspired Optimizaiton Methods and their Applications, BIOMA, 105-118.