- author = "Kathrin Kefer and Roland Hanghofer and Patrick Kefer and Markus Stoeger and Bernd Hofer and Michael Affenzeller and Stephan Winkler",
- title = "Multi Tree Operators for Genetic Programming to Identify Optimal Energy Flow Controllers",
- booktitle = "6th Workshop on Industrial Applications of Metaheuristics",
- year = "2021",
- editor = "Silvino Fernandez Alzueta and Thomas Stuetzle and Pablo Valledor",
- pages = "1579--1586",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Genetic Programming Operators, Symbolic Regression",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
10.1145/3449726.3463181",
- size = "8 pages",
- abstract = "Genetic programming is known to be able to find nearly optimal solutions for quite complex problems. So far, the focus was more on solution candidates that hold just one symbolic regression tree. For complex problems like controlling the energy flows of a building in order to minimize its energy costs, this is often not sufficient. This is why this work presents a solution candidate implementation in HeuristicLab where they hold multiple symbolic regression trees. Additionally, also new crossover and mutation operators were implemented as the existing ones cannot handle multiple trees in one solution candidate. The first type of operators applies them on all trees in the solution candidate, whereas the second one only applies them to one of the trees. It is found that applying the mutator to only one of the trees significantly reduces the training duration. Applying the crossover to one of the trees instead of all needs longer training times but can also achieve better results.",
-
notes = "Fronius International GmbH, Austria
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
