evortran: a modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction

TL;DR

evortran is a modern Fortran library that enables high-performance genetic algorithms for complex data fitting and parameter optimization in physics, with flexible strategies and demonstrated applications in LHC data analysis and gravitational wave reconstruction.

Contribution

The paper introduces evortran, a flexible, efficient Fortran package for genetic algorithms, with novel features like migration support and diverse strategies, tailored for physics applications.

Findings

Demonstrated evortran's effectiveness in LHC data fitting.

Showcased gravitational wave spectrum reconstruction from LISA data.

Compared performance favorably with existing frameworks.

Abstract

evortran is a modern Fortran library designed for high-performance genetic algorithms and evolutionary optimization. evortran can be used to tackle a wide range of problems in high-energy physics and beyond, such as derivative-free parameter optimization, complex search taks, parameter scans and fitting experimental data under the presence of instrumental noise. The library is built as an fpm package with flexibility and efficiency in mind, while also offering a simple installation process, user interface and integration into existing Fortran (or Python) programs. evortran offers a variety of selection, crossover, mutation and elitism strategies, with which users can tailor an evolutionary algorithm to their specific needs. evortran supports different abstraction levels: from operating directly on individuals and populations, to running full evolutionary cycles, and even enabling migration between independently evolving populations to enhance convergence and maintain diversity. In this paper, we present the functionality of the evortran library, demonstrate its capabilities with example benchmark applications, and compare its performance with existing genetic algorithm frameworks. As physics-motivated applications, we use evortran to confront extended Higgs sectors with LHC data and to reconstruct gravitational wave spectra and the underlying physical parameters from LISA mock data, demonstrating its effectiveness in realistic, data-driven scenarios.