TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming

Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 127–130

Abstract

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 6

1. Roman Kalkreuth Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

2. Fabrício Olivetti de França

   Universidade Federal do ABC

   Affiliation as printed

   Universidade Federal do ABC, Santo André, Brazil

3. Anja Jankovič Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

4. Marie Anastacio Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

5. Zdeněk Vašíček

   Brno University of Technology

   Affiliation as printed

   Brno University of Technology, Brno, Czech Republic

6. Holger H. Hoos Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany