TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
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.
Authors
- Marie Anastacio (ORCID: https://orcid.org/0000-0002-4039-2470)
- Fabrício Olivetti de França (ORCID: https://orcid.org/0000-0002-2741-8736)
- Holger H. Hoos (ORCID: https://orcid.org/0000-0003-0629-0099)
- Roman Kalkreuth (ORCID: https://orcid.org/0000-0003-1449-5131)
- Anja Jankovič (ORCID: https://orcid.org/0000-0001-9267-4595)
- Zdeněk Vašíček (ORCID: https://orcid.org/0000-0002-2279-5217)
- Julian Dierkes
Institutions
- Brno University of Technology (CZ)
- Universidade Federal do ABC (BR)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Proceedings of the Genetic and Evolutionary Computation Conference Companion
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1145/3712255.3726697
- Primary Topic
- Evolutionary Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Grantová Agentura České Republiky
- Conselho Nacional de Desenvolvimento Científico e Tecnológico