TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
This work addresses the need for standardized cross-domain comparisons in genetic programming, but it is incremental as it builds on existing frameworks like tinyGP.
The authors tackled the problem of fragmented benchmarking in genetic programming by proposing TinyverseGP, a unified framework that supports multiple representations and problem domains, including symbolic regression, logic synthesis, and policy search.
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.