Title: COBI: A Generator of Constrained Bi-Objective Test Problems with Known Optima

Speaker: Tea Tušar
Jozef Stefan Institute

Summary:
COBI is a new generator of scalable benchmark problems for COnstrained BI-iobjective optimization, designed to balance realistic properties with analytical tractability. It produces problems that capture key challenges of real-world scenarios, such as multimodality, ill-conditioning, and disconnected feasible regions, while still providing known Pareto sets and fronts. The construction of COBI problems is based on strictly convex-quadratic objectives and their multipeak extensions, combined with linear, convex-quadratic, or multipeak constraints, enabling analytical characterization and efficient computation of the Pareto set. In addition to introducing COBI problems and their properties, we will examine six strategies for approximating their Pareto sets, comparing them in terms of quality indicators and computational cost, and highlighting the trade-offs between accuracy and efficiency.

Bio:
Tea Tušar is a senior research associate at the Department of Intelligent Systems at the Jožef Stefan Institute and an assistant professor at the Jožef Stefan International Postgraduate School. She received her PhD for her work on visualizing solution sets in multi-objective optimization. Following her doctorate, she completed a postdoctoral fellowship at Inria Lille, France, where she contributed to benchmarking multi-objective optimization algorithms. Her work focuses on Evolutionary Computation, with particular emphasis on the visualization and benchmarking of evolutionary algorithms for single- and multi-objective optimization, including constrained scenarios, as well as their application to real-world problems.

She has participated in several collaborative projects involving the application of optimization techniques to problems such as electric motor design, energy scheduling, and tunnel alignment.

She has contributed to the development of the COCO platform (https://coco-platform.org/) for comparing optimization algorithms, expanding its capabilities to handle multi-objective and mixed-integer problems. She has held organizational and editorial roles at international conferences, including GECCO, PPSN, and EMO, and serves as an associate editor for Evolutionary Computation and ACM Transactions on Evolutionary Learning and Optimization. Since 2025, she has been serving as President of ACM

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SEMINAR: WED 15 APRIL 2026 16:00-17:00 (AEST, Melbourne)/ 08:00-09:00 (CEST)