DynStack

Dynamic optimization problems (DOPs) are an underrepresented class in benchmarking evolutionary computation systems (ECS). Most benchmarks focus on more or less expensive problems, but which never change during the optimization. In real-world logistics operations however, dynamic changes and even uncertainty are natural and have to be dealt with. While evolutionary algorithms are certainly well suited methods to tackle such problems, the field lacks public and open source, easy-to-use, but still complex dynamic environments for comparing and further developing the methods. In this work, we highlight the framework that we have created and open sourced as part of the DynStack competition which was first held at GECCO 2020. We present the underlying principles of the framework, the architecture that eases the application, and potential ways to benchmark a range of methods. The environments implemented in this framework are real-world industrial scenarios, that have been simplified, but which still convey practical challenges in the application of ECS to real-world problems.