@inproceedings{af695091af674afc9309d27325de14d9,
title = "The inefficiency of genetic programming for symbolic regression",
abstract = "We analyse the search behaviour of genetic programming (GP) for symbolic regression (SR) in search spaces that are small enough to allow exhaustive enumeration, and use an improved exhaustive symbolic regression algorithm to generate the set of semantically unique expression structures, which is orders of magnitude smaller than the original SR search space. The efficiency of GP and a hypothetical random search in this set of unique expressions is compared, whereby the efficiency is quantified via the number of function evaluations performed until a given error threshold is reached, and the percentage of unique expressions evaluated during the search after simplification to a canonical form. The results for two real-world datasets with a single input variable show that GP in such limited search space explores only a small fraction of the search space, and evaluates semantically equivalent expressions repeatedly. GP has a smaller success probability than the idealised random search for such small search spaces.",
keywords = "Symbolic regression, Genetic programming, Search space",
author = "Gabriel Kronberger and {Olivetti de Franca}, Fabricio and Harry Desmond and Bartlett, {Deaglan J.} and Lukas Kammerer",
year = "2024",
month = sep,
day = "7",
doi = "10.1007/978-3-031-70055-2_17",
language = "English",
isbn = "9783031700545",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "273--289",
editor = "Michael Affenzeller and Winkler, {Stephan M.} and Kononova, {Anna V.} and Heike Trautmann and Tea Tu{\v s}ar and Penousal Machado and Thomas B{\"a}ck",
booktitle = "Parallel Problem Solving from Nature – PPSN XVIII",
note = "Parallel Problem Solving from Nature – PPSN XVIII: 18th International Conference, PPSN 2024 ; Conference date: 14-09-2024 Through 18-09-2024",
url = "https://ppsn2024.fh-ooe.at/",
}