@inproceedings{3d66dcfa38174dd0b0c5f2de7590511d,
title = "Fitness landscape analysis of dimensionally-aware genetic programming featuring feynman equations",
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{\textquoteright}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.",
keywords = "Dimensionally-Aware GP, Fitness landscape, Genetic programming, Local optima network",
author = "M. {\DH}urasevi{\'c} and Domagoj Jakobovic and Martins, {Marcella Scoczynski Ribeiro} and Stjepan Picek and Markus Wagner",
note = "Accepted author manuscript; 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 ; Conference date: 05-09-2020 Through 09-09-2020",
year = "2020",
doi = "10.1007/978-3-030-58115-2_8",
language = "English",
isbn = "978-3-030-58114-5",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "111--124",
editor = "Thomas B{\"a}ck and Mike Preuss and Andr{\'e} Deutz and Michael Emmerich and Hao Wang and Carola Doerr and Heike Trautmann",
booktitle = "Parallel Problem Solving from Nature – PPSN XVI",
edition = "Part II",
}