As part of the CAMELOT project, the genetic programming framework GeneticEngine was submitted to GECCO’22 Symbolic Regression competition, achieving 3rd place in the Real-World track in a case study of predicting covid cases in New York. Since then, the LASIGE team of Leon Ingelse, Guilherme Espada, and Alcides Fonseca contributed to the analysis of the state of the art in symbolic regression in the SRBench++ paper, published in the IEEE Transactions on Evolutionary Computation, a SCIMAGO Q1 journal with a impact factor of 11.7.

If you want to figure out what mathematical expression (e.g., imc = weight/(height^2) ) from datasets of measurements, you can use GeneticEngine. Genetic Engine also scales to larger problems such as synthesis of computer programs.