TY - JOUR
T1 - Adaptive Operators for Genetic Programming to Identify Optimal Energy Flow Controllers
AU - Kefer, Kathrin
AU - Affenzeller, Michael
AU - Winkler, Stephan
N1 - Publisher Copyright:
© 2024 The Authors. Published by Elsevier B.V.
PY - 2025
Y1 - 2025
N2 - Genetic programming can find nearly optimal solutions for complex problems like minimizing a building's energy costs by optimally controlling its energy flows. For such problems, usually multiple controllers are necessary. In order to allow a faster convergence in combination with a more fine-grained and directed search, this work presents new adaptive crossover and mutation operators. Instead of applying the operators always to all symbolic regression trees in a solution candidate, the new operators are applied to all trees only in the beginning and then to a randomly chosen group of them as soon as a threshold is reached. Towards the end of the training, the adaptive operators then switch to applying crossover and mutation to only one of the trees in a solution candidate for a more fine-grained search. Additionally, a new crossover is proposed where the children solution candidates are themselves evaluated for their performance before promoting one of them to the next generation in order to assure a more directed search. To evaluate these new operators, a total of twelve energy management controllers is trained with the Offspring Selection Genetic Algorithm and are evaluated for training results in form of the needed number of evaluated solutions and generations as well as their ability to reduce the energy costs and their learned behaviour. Results show that the proposed adaptive operators achieve very similar results to the baseline optimization and that the Best Child crossover is the fastest to converge.
AB - Genetic programming can find nearly optimal solutions for complex problems like minimizing a building's energy costs by optimally controlling its energy flows. For such problems, usually multiple controllers are necessary. In order to allow a faster convergence in combination with a more fine-grained and directed search, this work presents new adaptive crossover and mutation operators. Instead of applying the operators always to all symbolic regression trees in a solution candidate, the new operators are applied to all trees only in the beginning and then to a randomly chosen group of them as soon as a threshold is reached. Towards the end of the training, the adaptive operators then switch to applying crossover and mutation to only one of the trees in a solution candidate for a more fine-grained search. Additionally, a new crossover is proposed where the children solution candidates are themselves evaluated for their performance before promoting one of them to the next generation in order to assure a more directed search. To evaluate these new operators, a total of twelve energy management controllers is trained with the Offspring Selection Genetic Algorithm and are evaluated for training results in form of the needed number of evaluated solutions and generations as well as their ability to reduce the energy costs and their learned behaviour. Results show that the proposed adaptive operators achieve very similar results to the baseline optimization and that the Best Child crossover is the fastest to converge.
KW - Adaptive Genetic Programming Operators
KW - Building Energy Management
KW - Energy Flow Controller Optimization
KW - Genetic Programming
KW - Genetic Programming Operators
KW - Renewable Energy
KW - Symbolic Regression
UR - http://www.scopus.com/inward/record.url?scp=105000500300&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2025.01.261
DO - 10.1016/j.procs.2025.01.261
M3 - Conference article
AN - SCOPUS:105000500300
SN - 1877-0509
VL - 253
SP - 1991
EP - 2002
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024
Y2 - 13 November 2024 through 15 November 2024
ER -