Webinar #28: Frequency Fitness Assignment
Speaker: Thomas Weise, Hefei University
Date: 29 September 2025
Time: 5:00 – 6:00pm (Beijing Time, UTC+8)
Prof. Dr. Thomas WEISE (汤卫思) holds a full professorship at Hefei University (合肥大学) in the city Hefei (合肥市) in the province Anhui (安徽省) in China. He received his Diplom-Informatiker in Computer Science from the Chemnitz University of Technology (德国开姆尼茨工业大学) in Chemnitz, Germany (德国开姆尼茨市) in 2005 and his doctoral degree (博士) from the University of Kassel (德国卡塞尔大学) in Kassel, Germany (德国卡 塞尔市) in 2009. Prof. Weise joined the School of Computer Science and Technology (计算机科学与技术学院) of the University of Science and Technology of China (USTC, 中国科学技术大学) in Hefei as PostDoc (博士后) from 2009 to 2011. He then was promoted to Associate Professor (副教授) in the same school. In 2016, Prof. Weise moved to Hefei University (合肥大学) to found the Institute of Applied Optimization (IAO, 应用优化研究所), which he leads as director, at the School of Artificial Intelligence and Big Data (人工智能与大数据学院). Prof. Weise holds the 2025 Huangshan Friendship Award of Anhui Province (安徽省黄山友谊奖) and the 2020 Hefei City Friendship Award (外国专家“友谊奖”).
The research field of Prof. Weise is metaheuristic optimization, where he made contributions to both fundamental and applied research. Prof. Weise is the author of over 50 journal articles and 79 conference papers. He has authored 139 peer-reviewed scientific publications in total. According to GoogleScholar, his work has been cited more than 4500 times and he has a h-index of 30 and an i10-index of 59. He has authored or co-authored works in journals such as the IEEE Transactions on Evolutionary Computation, the IEEE Computational Intelligence Magazine, the IEEE Transactions on Image Processing, Information Fusion, Pattern Recognition, Information Sciences, Applied Soft Computing, Soft Computing, the European Journal of Operational Research, Evolutionary Computation, the Journal of Global Optimization, the Journal of Computer Science & Technology, and the Journal of Combinatorial Optimization. Prof. Weise is editorial board member of Applied Soft Computing and reviewer for over 30 journals and over 70 conferences. His book “Global Optimization Algorithms — Theory and Application” has been cited more than 1300 times.
Optimization problems are situations where we have to pick one of many possible choices and want to do this in such a way that we reach a pre-defined goal at a minimum cost. Classical optimization problems include the Traveling Salesperson Problem, the Maximum Satisfiability Problem (MaxSAT), and the Bin Packing problem, for example. Since these problems are NP-hard and solving them to optimality would require exponential runtime in the worst case, metaheuristic algorithms have been developed that deliver near-optimal solutions in acceptable runtime. Examples for classical metaheuristics are the (1+1) EA, Simulated Annealing (SA), and the Standard Genetic Algorithm (SGA). Since we want that such algorithms should behave the same in both quick benchmarking experiments and in practical application, we would like them to exhibit invariance properties. Whereas the (1+1) EA is invariant under all order-preserving transformations of the objective function value, SA is not invariant under scaling of the objective function and the SGA is not invariant under translations of the objective function. Frequency Fitness Assignment (FFA) is an algorithm module that can be plugged into existing algorithms and makes them invariant under all injective transformations of the objective function value (which goes far beyond order-preserving transformations). We plug FFA into the (1+1) EA. We show that the resulting (1+1) FEA can solve Trap, TwoMax, and Jump problems in polynomial runtime, whereas the (1+1) EA needs exponential runtime. Moreover, the (1+1) FEA performs very significantly faster on the NP-hard MaxSAT problem. We conclude the presentation with an outline of other properties of FFA and our other recent works.
The Webinar went very successful.
The slides of the Webinar can be found here.