evortran: a modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction
A high-performance genetic algorithm library is bridging particle physics and astrophysics...
Researchers have released evortran, a modern Fortran library for high-performance genetic algorithms and evolutionary optimization. The package is designed for complex, data-driven physics problems, enabling derivative-free parameter optimization and fitting experimental data with instrumental noise. It offers various selection, crossover, and mutation strategies, and now includes a Python interface. The library has been demonstrated on two major applications: confronting extended Higgs sectors with LHC data and reconstructing gravitational wave spectra from LISA mock data.
Why It Matters
This provides physicists with a powerful, flexible tool for optimizing complex models where traditional gradient-based methods fail.