Michaela Meinert

Lunchtime Seminar - Evolutionary Computing for Problems with Dynamically Changing Constraints

Tuesday, 13. November 2018 - 12:00 to Friday, 21. June 2024 - 19:57, Leonardo-Campus 18

Speaker:   Prof. Frank Neumann

Abstract:   Dynamic problems appear frequently in real-world applications such as logistics for mining and are usually subject to a large set of constraints. These constraints change over time due to changes in resources and having algorithms that can deal with such dynamic changes delivers direct benefit to decision makers. Evolutionary algorithms are well suited for such dynamic problems as they can easily adapt to changing environments. In this talk, I will report on some theoretical and experimental investigations that we have carried out in the area of evolutionary algorithms for problems with dynamic constraints. The focus will be the classical knapsack problem where the given constraint bound changes over time.

Short Bio:   Frank Neumann received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively. Currently, he is a full Professor and leader of the Optimisation and Logistics Group at the School of Computer Science, The University of Adelaide, Australia. Frank has been the general chair of the ACM Genetic and Evolutionary Computation Conference (GECCO) 2016. He co-organised ACM Foundations of Genetic Algorithms (FOGA) 2013 and is an author of the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer.  Currently, he is an Associate Editor of the journals "Evolutionary Computation" (MIT Press, IF: 3.826) and "IEEE Transactions on Evolutionary Computation" (IF: 10.629).  In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of creativity, renewable energy, logistics, and mining.