This paper introduces PyGAD, an open-source easy-to-use Python library for building the genetic algorithm. PyGAD supports a wide range of parameters to give the user control over everything in its life cycle. This includes, but is not limited to, population, gene value range, gene data type, parent selection, crossover, and mutation. PyGAD is designed as a general-purpose optimization library that allows the user to customize the fitness function. Its usage consists of 3 main steps: build the fitness function, create an instance of the pygad.GA class, and calling the pygad.GA.run() method. The library supports training deep learning models created either with PyGAD itself or with frameworks like Keras and PyTorch. Given its stable state, PyGAD is also in active development to respond to the user's requested features and enhancement received on GitHub https://github.com/ahmedfgad/GeneticAlgorithmPython. PyGAD comes with documentation https://pygad.readthedocs.io for further details and examples.
翻译:本文介绍PyGAD,这是一个用于建立遗传算法的开放源码方便使用的 PyGAD 图书馆。 PyGAD 支持一系列广泛的参数,使用户能够控制其生命周期中的所有事物,这包括但不限于人口、基因价值范围、基因数据类型、父系选择、交叉和突变。PyGAD 设计为通用优化图书馆,使用户能够定制健身功能。它的用法由三个主要步骤组成:建立健身功能,创建 pygad.GA 类实例,并调用 pygad.GA.run () 的方法。图书馆支持培训与Pygad. GA. run () 本身或与Keras 和 PyTorrch 等框架创建的深层次学习模式。鉴于其稳定状态, PyGAD 也在积极开发中,以响应用户在 GitHub https://github.com/ahmedfgad/GenticalthmPython 上要求的功能和增强功能。 PyGyGyGyGADAD提供文件的示例。