扫码阅读
手机扫码阅读

geatpy遗传算法包介绍(一)

195 2024-10-27

我们非常重视原创文章,为尊重知识产权并避免潜在的版权问题,我们在此提供文章的摘要供您初步了解。如果您想要查阅更为详尽的内容,访问作者的公众号页面获取完整文章。

查看原文:geatpy遗传算法包介绍(一)
文章来源:
Python学习杂记
扫码关注公众号
Geatpy Summary

Geatpy Overview

Geatpy is an open-source genetic algorithm package developed by several universities in China. It is a high-performance, practical evolutionary algorithm toolbox that offers library functions for important operations within various implemented evolutionary algorithms. With its highly modular and loosely coupled object-oriented evolutionary algorithm framework, Geatpy employs a "define problem class + call algorithm template" pattern for evolutionary optimization, suitable for solving single-objective optimization, multi-objective optimization, complex constraint optimization, combinatorial optimization, and mixed encoding evolutionary optimization.

Basic Problem Solving

The process of finding the optimal solution using the Geatpy package involves two main steps: the first step is constructing the problem framework, which includes writing the objective function and constraint functions; the second step is problem-solving. A brief example of solving a single-objective problem is presented.

Building the Problem Framework

The process of defining the problem starts with importing the necessary modules and writing the problem class, which includes the objective function, constraints, and other components. A code snippet of writing the problem framework is provided. It defines a maximization problem with the objective function f = x * np.sin(10 * np.pi * x) + 2.0 and a constraint -1 ≤ x ≤ 2.

Due to incomplete content provided, the summary cannot cover the entire process of problem-solving with Geatpy, including the second step of the basic problem-solving example.

This HTML summary provides an overview of Geatpy, its utility, and the first step of problem-solving using the package. However, the incomplete content provided does not allow for a complete summary of the problem-solving process, including the second step.

想要了解更多内容?

查看原文:geatpy遗传算法包介绍(一)
文章来源:
Python学习杂记
扫码关注公众号