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Detail publikačního výsledku
KALKREUTH, R.; DE, O.; JANKOVIC, A.; ANASTACIO, M.; DIERKES, J.; VAŠÍČEK, Z.; HOOS, H.
Originální název
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
Anglický název
Druh
Stať ve sborníku mimo WoS a Scopus
Originální abstrakt
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.
Anglický abstrakt
Klíčová slova
Genetic Programming, Implementation, Benchmarking, Symbolic Regression, Logic Synthesis, Python
Klíčová slova v angličtině
Autoři
Vydáno
14.07.2025
Nakladatel
Association for Computing Machinery
Místo
Malaga
ISBN
979-8-4007-1464-1
Kniha
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Strany od
2172
Strany do
2176
Strany počet
5
BibTex
@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" }