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. His research interests focus on numerical and evolutionary optimization with an emphasis on multi-objective optimization problems. He has co-authored more than 170 publications including 2 monographic books, 5 text books and 17 edited books. Google Scholar reports more than 4,500 citations and a Hirsch index of 35. 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), and is recipient of the H. S. Hsu Award 2022. He is Editor-in-Chief of the journal Mathematical and Computational Applications, and member of the Editorial Board for Applied Soft Computing, Computational Optimization and Applications, Engineering Optimization, Results in Control and Optimization, and IEEE Transactions on Evolutionary Computation. He is founder of the workshop series Numerical and Evolutionary Optimization (NEO). Dr. Schuetze is member of the Mexican Academy of Sciences (AMC) and the National Network of Researchers (SNI Level III).

For more information about Oliver Schütze go to https://neo.cinvestav.mx/Group/.

Bernardino Romera-Paredes (Google DeepMind) is a former core team member of AlphaFold2 and AlphaTensor and now research scientist at Google DeepMind in London. At PPSN 2024 Bernardino Romera-Paredes will present his current research regarding the evolution of new heuristics, supported by pairing a pre-trained LLM and an automated "evaluator", with his keynote "FunSearch: Discovering new mathematics and algorithms using Large Language Models". In this talk I will present FunSearch, a method to search for new solutions in mathematics and computer science. FunSearch works by pairing a pre-trained LLM, whose goal is to provide creative solutions in the form of computer code, with an automated “evaluator”, which guards against hallucinations and incorrect ideas. By leveraging these two components within an evolutionary algorithm, initial solutions “evolve” into new knowledge. I will present the application of FunSearch to a central problem in extremal combinatorics — the cap set problem — where we discover new constructions of large cap sets going beyond the best known ones, both in finite dimensional and asymptotic cases. This represents the first discoveries made for established open problems using LLMs. Then, I will present the application of FunSearch to an algorithmic problem, online bin packing, which showcases the generality of the method. In this use case, FunSearch finds new heuristics that improve upon widely used baselines. I will conclude the talk by discussing the implications of searching in the space of code.

For more information about Bernardino Romera-Paredes go to https://www.romera-paredes.com/.