Detail výsledku
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
DE, O.
JANKOVIC, A.
ANASTACIO, M.
DIERKES, J.
Vašíček Zdeněk, doc. Ing., Ph.D., UPSY (FIT)
HOOS, H.
Over the years, genetic programming (GP) has evolved, with many proposed
variations, especially in how they represent a solution. Being essentially
a program synthesis algorithm, it is capable of tackling multiple problem
domains. Current benchmarking initiatives are fragmented, as the different
representations are not compared with each other and their performance is not
measured across the different domains. In this work, we propose a unified
framework, dubbed TinyverseGP (inspired by tinyGP), which provides support to
multiple representations and problem domains, including symbolic regression,
logic synthesis and policy search.
Genetic Programming, Implementation, Benchmarking, Symbolic Regression, Logic
Synthesis, Python
@inproceedings{BUT197539,
author="KALKREUTH, R. and DE, O. and JANKOVIC, A. and ANASTACIO, M. and DIERKES, J. and VAŠÍČEK, Z. and HOOS, H.",
title="TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming",
booktitle="Proceedings of the Genetic and Evolutionary Computation Conference Companion",
year="2025",
pages="2172--2176",
publisher="Association for Computing Machinery",
address="Malaga",
doi="10.1145/3712255.3726697",
isbn="979-8-4007-1464-1"
}