Evolutionary Computing for Problems with Dynamically Changing Constraints
08.07.2019 von 11:00 bis 12:00
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 and constrained submodular
functions where the given constraint bound changes over time.
Based on:
-V. Roostapour, A. Neumann, F. Neumann (2018): On the performance of
baseline evolutionary algorithms on the dynamic knapsack problem.
In: Parallel Problem Solving from Nature XV, PPSN 2018.
-V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2019): Pareto
optimization for subset selection with dynamic cost constraints.
In: Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019.