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Abstract
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.
Original language | English |
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Title of host publication | GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference |
Publisher | Association for Computing Machinery, Inc |
Pages | 1984-1991 |
Number of pages | 8 |
ISBN (Electronic) | 9781450392686 |
DOIs | |
Publication status | Published - 9 Jul 2022 |
Event | 2022 Genetic and Evolutionary Computation Conference, GECCO 2022 - Virtual, Online, United States Duration: 9 Jul 2022 → 13 Jul 2022 |
Publication series
Name | GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference |
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Conference
Conference | 2022 Genetic and Evolutionary Computation Conference, GECCO 2022 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 09.07.2022 → 13.07.2022 |
Keywords
- benchmark
- dynamic optimization problem
- operations research
- software framework
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SCHED-ENERGY - Adaptive scheduling in multi-stage production systems with sensor-based predictions for reducing energy consumption
Wagner, S. (CoPI), Altendorfer, K. (CoPI), Bindreiter, O. (CoI), Bokor, B. (CoI), Wagner, S. (PI), Holzinger, F. C. (CoI) & Seiringer, W. (CoI)
01.04.2022 → 31.03.2026
Project: Research Project