- author = "Joerg Bremer and Sebastian Lehnhoff",
- title = "Learning Parameterizable Decoders with Cartesian Genetic Programming",
- booktitle = "24th UK Workshop on Computational Intelligence (UKCI 2025)",
- year = "2025",
- editor = "Emma Hart and Tomas Horvath and Zhiyuan Tan and Sarah Thomson",
- volume = "1468",
- series = "Advances in Intelligent Systems and Computing",
- pages = "79--90",
- address = "Edinburgh Napier University",
- month = "3--5 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Decoder, Constraint Handling, Solution Repair, Predictive Scheduling",
- isbn13 = "978-3-032-07937-4",
-
DOI = "
10.1007/978-3-032-07938-1_7",
- abstract = "For optimization in the smart grid, distributed algorithms based on decoders for handling individual constraints of different energy resources are a promising approach to tackle the scalability and versatility of controlled devices. Decoders based on machine learning can capture the operational capabilities and serve as a means for systematically ensuring the feasibility of solution candidates during optimization. Currently, decoders are trained based on a training set for a specific initial state predicted for the start time of the optimization period. Thus, a new decoder has to be trained for any new initial operational state of the energy resource. This paper explores a new approach based on Cartesian genetic programming to train a decoder that can be parameterized with different initial states. We train decoders for co-generation plants with a range of different states of charge for the thermal buffer store and demonstrate that such decoders can be obtained in a reasonable training time and with sufficiently good performance over the whole range of temperatures.",
-
notes = "Published after the conference.
University of Oldenburg, 26129, Oldenburg, Germany",
