Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and Adaptability
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{fong:2023:GECCOcomp,
-
author = "Kei Sen Fong and Shelvia Wongso and Mehul Motani",
-
title = "Evolutionary Symbolic Regression: Mechanisms from the
Perspectives of Morphology and Adaptability",
-
booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
-
year = "2023",
-
editor = "Alberto Moraglio",
-
pages = "21--22",
-
address = "Lisbon, Portugal",
-
series = "GECCO '23",
-
month = "15-19 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming, symbolic
regression",
-
isbn13 = "9798400701191",
-
DOI = "doi:10.1145/3583133.3595830",
-
size = "2 pages",
-
abstract = "Symbolic Regression (SR) is the task of finding
closed-form analytical expressions that describe the
relationship between variables in a dataset. In this
work, werethink SR and introduce mechanisms from two
perspectives: morphology and adaptability. Morphology:
Man-made heuristics are typically used in SR algorithms
to influence the morphology (or structure) of candidate
expressions, potentially introducing unintentional bias
and data leakage. To address this issue, we create a
depth-aware mathematical language model trained on
terminal walks of expression trees, as a replacement to
these heuristics. Adaptability: We promote alternating
fitness functions across generations, eliminating
equations that perform well in only one fitness
function and as a result, discover expressions that are
closer to the true functional form. We demonstrate this
by alternating fitness functions that quantify
faithfulness to values (via MSE) and empirical
derivatives (via a novel theoretically justified
fitness metric coined MSEDI). Proof-of-concept: We
combine these ideas into a minimalistic evolutionary SR
algorithm that outperforms a suite of benchmark and
state of-the-art SR algorithms in problems with unknown
constants added, which we claim are more reflective of
SR performance for real-world applications. Our claim
is then strengthened by reproducing the superior
performance on real-world regression datasets from
SRBench. This Hot-of-the-Press paper summarizes the
work K.S. Fong, S. Wongso and M. Motani, {"}Rethinking
Symbolic Regression: Morphology and Adaptability in the
Context of Evolutionary Algorithms{"}, The Eleventh
International Conference on Learning International
Conference on Learning Representations (ICLR'23).",
-
notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Kei Sen Fong
Shelvia Wongso
Mehul Motani
Citations