Print Email Facebook Twitter Fitness landscape analysis of dimensionally-aware genetic programming featuring feynman equations Title Fitness landscape analysis of dimensionally-aware genetic programming featuring feynman equations Author Ðurasević, M. (University of Zagreb) Jakobovic, Domagoj (University of Zagreb) Martins, Marcella Scoczynski Ribeiro (Federal University of Technology - Paraná (UTFPR)) Picek, S. (TU Delft Cyber Security) Wagner, Markus (University of Adelaide) Contributor Bäck, Thomas (editor) Preuss, Mike (editor) Deutz, André (editor) Emmerich, Michael (editor) Wang, Hao (editor) Doerr, Carola (editor) Trautmann, Heike (editor) Date 2020 Abstract Genetic programming is an often-used technique for symbolic regression: finding symbolic expressions that match data from an unknown function. To make the symbolic regression more efficient, one can also use dimensionally-aware genetic programming that constrains the physical units of the equation. Nevertheless, there is no formal analysis of how much dimensionality awareness helps in the regression process. In this paper, we conduct a fitness landscape analysis of dimensionally-aware genetic programming search spaces on a subset of equations from Richard Feynman’s well-known lectures. We define an initialisation procedure and an accompanying set of neighbourhood operators for conducting the local search within the physical unit constraints. Our experiments show that the added information about the variable dimensionality can efficiently guide the search algorithm. Still, further analysis of the differences between the dimensionally-aware and standard genetic programming landscapes is needed to help in the design of efficient evolutionary operators to be used in a dimensionally-aware regression. Subject Dimensionally-Aware GPFitness landscapeGenetic programmingLocal optima network To reference this document use: http://resolver.tudelft.nl/uuid:3d66dcfa-3817-4dd0-b0c5-f2de7590511d DOI https://doi.org/10.1007/978-3-030-58115-2_8 Publisher Springer, Cham Embargo date 2021-09-02 ISBN 978-3-030-58114-5 Source Parallel Problem Solving from Nature – PPSN XVI (Part II) Event 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, 2020-09-05 → 2020-09-09, Leiden, Netherlands Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12270 Bibliographical note Accepted author manuscript Part of collection Institutional Repository Document type conference paper Rights © 2020 M. Ðurasević, Domagoj Jakobovic, Marcella Scoczynski Ribeiro Martins, S. Picek, Markus Wagner Files PDF DAGP_FLA.pdf 1013.9 KB Close viewer /islandora/object/uuid:3d66dcfa-3817-4dd0-b0c5-f2de7590511d/datastream/OBJ/view