- author = "Fabian Koehnke and Christian Borgelt",
- title = "Variable-Size Genetic Network Programming for Portfolio Optimization with Trading Rules",
- booktitle = "28th International Conference, EvoApplications 2025",
- year = "2025",
- editor = "Pablo Garcia-Sanchez and Emma Hart andx1 Sarah L. Thomson",
- series = "LNCS",
- volume = "15612",
- publisher = "Springer",
- address = "Trieste, Italy",
- month = "3-5 " # apr,
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, GNP, Variable-Size Genetic Network Programming, Portfolio Optimization, Trading Rules, Time Series Analysis, Evolutionary Algorithms, Genetic Operators",
- isbn13 = "978-3-031-90062-4",
- timestamp = "Sat, 31 May 2025 23:09:36 +0200",
-
biburl = "
https://dblp.org/rec/conf/evoapps/KohnkeB25.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
DOI = "
10.1007/978-3-031-90062-4_18",
- abstract = "We present an extension of a graph-based evolutionary algorithm called Genetic Network Programming (GNP) by a novel mutation operator, which allows for a variable number of nodes and edges per individual. With this operator, the search space is significantly extended, but without the risk of incurring the bloat problem. The operator is fitness neutral and has no hyper-parameter. Due to higher flexibility, it is now possible for GNP to automatically adapt to the complexity of a given task and to find suitable features, especially for high dimensional data sets. We applied our mutation operator successfully in a GNP for a financial data set where it improved over standard GNP with an optimal network size while maintaining the interpretability of the solution candidates.",
-
notes = "DBLP:conf/evoapps/KohnkeB25,
Department of Artificial Intelligence, University of Salzburg, Salzburg, Austria
http://www.evostar.org/2025/ EvoApplications2025 held in conjunction with EuroGP'2025, EvoCOP2025 and EvoMusArt2025",
