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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.10253 |
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| _version_ | 1866915241395224576 |
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| author | Kalkreuth, Roman de França, Fabricio Olivetti Dierkes, Julian Anastacio, Marie Jankovic, Anja Vasicek, Zdenek Hoos, Holger |
| author_facet | Kalkreuth, Roman de França, Fabricio Olivetti Dierkes, Julian Anastacio, Marie Jankovic, Anja Vasicek, Zdenek Hoos, Holger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming Kalkreuth, Roman de França, Fabricio Olivetti Dierkes, Julian Anastacio, Marie Jankovic, Anja Vasicek, Zdenek Hoos, Holger Neural and Evolutionary Computing Machine Learning Symbolic Computation 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. |
| title | TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming |
| topic | Neural and Evolutionary Computing Machine Learning Symbolic Computation |
| url | https://arxiv.org/abs/2504.10253 |