NEO 2025, Tijuana, BC, Mexico, September 23 - 26, 2025

Chair: Dr. Oliver Schütze

In many applications one is faced with the problem to optimize several objectives concurrently. One important characteristic of such multi-objective optimization problems (MOPs) is that their solution sets -- the so-called Pareto sets and fronts -- form objects of dimension k-1, where k is the number of objectives involved in the problem. For the treatment of MOPs, specialized evolutionary strategies -- multi-objective evolutionary algorithms (MOEAs) -- have caught the interest of many researchers and practitioners over the last decades. No continuity or differentiability assumptions are required by EMO algorithms, and problem characteristics such as nonlinearity, multimodality and stochasticity can be handled as well. Furthermore, preference information provided by a decision maker can be used to deliver a finite-size approximation to the optimal solution set in a single optimization run.

The Evolutionary Multiobjective Optimization (EMO) session is intended to bring together researchers working in this and related areas to discuss all aspects of EMO development and deployment, including (but not limited to):

• Handling of continuous, combinatorial or mixed-integer problems
• Test problems and performance assessment
• Benchmarking studies, especially in comparison to non-EMO methods
• Selection mechanisms
• Variation mechanisms
• Hybridization
• Theoretical foundations and search space analysis that bring new insights to EMO
• Implementation aspects
• Preference articulation
• Interactive optimization
• Many-objective optimization
• Large-scale optimization
• Expensive function evaluations
• Constraint handling
• Uncertainty handling
• Real-world applications

Oliver Schütze received a PhD in Mathematics from the University of Paderborn, Germany, in 2004. He is currently professor at the Cinvestav-IPN in Mexico City, Mexico.
The research interests of Dr. Schütze focus on numerical and evolutionary optimization where he addresses both numerical and evolutionary techniques. He has co-authored more than 170 publications including 2 monographic books, 5 text books and 16 edited books. Google Scholar reports more than 4,900 citations and a Hirsch index of 36. During his career he received several prices and awards. For instance, he is co-author of two papers that won the IEEE CIS Outstanding Paper Award (for the IEEE TEC papers of 2010 and 2012). He is recipient of the C. S. Hsu Award 2022.
He is Editor-in-Chief of the journal Mathematical and Computational Applications, and member of the Editorial Board for IEEE Transactions on Evolutionary Computation, Applied Soft Computing, Computational Optimization and Applications, Engineering Optimization, and Research in Control and Optimization.
Dr. Schuetze is member of the Mexican Academy of Sciences (AMC) and the National Network of Researchers (SNI Level III). More information about Dr. Schütze and his research team can be found at: https://neo.cinvestav.mx/Group/index.php