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.

翻译：evortran 是一个专为高性能遗传算法与进化优化设计的现代 Fortran 库。evortran 可用于解决高能物理及其他领域中的各类问题，例如无导数参数优化、复杂搜索任务、参数扫描以及在仪器噪声存在下的实验数据拟合。该库构建为一个 fpm 软件包，兼顾灵活性与效率，同时提供简单的安装过程、用户界面以及与现有 Fortran（或 Python）程序的集成能力。evortran 提供了多种选择、交叉、变异和精英保留策略，用户可根据具体需求定制进化算法。evortran 支持不同的抽象层级：从直接操作个体和种群，到运行完整的进化周期，甚至支持独立演化种群之间的迁移以增强收敛性并保持多样性。本文介绍了 evortran 库的功能，通过示例基准应用展示了其性能，并与现有遗传算法框架进行了比较。作为物理驱动的应用案例，我们使用 evortran 将扩展希格斯扇区与 LHC 数据进行对比，并从 LISA 模拟数据中重建引力波谱及潜在物理参数，证明了其在现实数据驱动场景中的有效性。