Learning Parameterizable Decoders with Cartesian Genetic Programming

Jörg Bremer

doi:10.1007/978-3-032-07938-1_7

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Learning Parameterizable Decoders with Cartesian Genetic Programming

2026-01-01

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Jörg Bremer *

Sebastian Lehnhoff

DOI：10.1007/978-3-032-07938-1_7

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Abstract

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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.

Keywords:

Decoder

Cartesian Genetic Programming

Constraint Handling

Solution Repair

Predictive Scheduling