Inspyred 1.0 is an open-source, freely available Python module developed by Dr. Aaron Garrett of Wofford College, Spartanburg, South Carolina, USA (https ://aaron garre tt.githu b.io/woffo rd-webs/). The project is under active development, with the latest updates released in January 2019. Inspyred provides Python implementations for some of the most commonly used Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies, Differential Evolution, Pareto Archived Evolutionary Strategy, and NSGA-II) and other bioinspired optimization techniques (ant colony optimization, particle swarm optimization, simulated annealing, and swarm intelligence). While Inspyred’s tools can be used as out-of-the-box optimization resources, its most commendable feature is its design methodology for EAs, explicitly “inspired” (pun intended) by De Jong’s 2006 book Evolutionary Computation: A Unified Approach. Inspyred implements a generic Evolutionary Computation as a series of components/Python functions: Problem-specific components

中文翻译：

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Inspyred：Python 中的仿生算法

Inspyred 1.0 是由美国南卡罗来纳州斯帕坦堡 Wofford 学院的 Aaron Garrett 博士开发的开源、免费的 Python 模块 (https://aaron garre tt.githu b.io/woffo rd-webs/)。该项目正在积极开发中，最新更新于 2019 年 1 月发布。 Inspyred 为一些最常用的进化算法（遗传算法、进化策略、差分进化、帕累托存档进化策略和 NSGA-II）提供 Python 实现和其他仿生优化技术（蚁群优化、粒子群优化、模拟退火和群智能）。虽然 Inspyred 的工具可以用作开箱即用的优化资源，但其最值得称道的功能是其针对 EA 的设计方法，De Jong 2006 年出版的 Evolutionary Computation: A Unified Approach 一书明确“启发”（双关语）。Inspyred 将通用进化计算实现为一系列组件/Python 函数：特定于问题的组件