湖南理工学院
人工智能课程论文
题目：遗传算法及其应用
课程名称：人工智能及其应用
院系：计算机学院
专业班级：计科13 - 2 BJ 姓名：李中文
学号: 14132404129
课程论文成绩：
指导教师：廖军
2015 年 6 月30 日
遗传算法及其应用
摘要：遗传算法(genetic algorithms,GA)是一类借鉴生物界自然选择和自然遗传机制的随机搜索算法，非常适用于处理传统搜索方法难以解决的复杂和非线性问题。遗传算法是基于达尔文进化论，在计算机上模拟生命进化机制而发展起来的一门新学科。它根据适者生存，优胜劣汰等自然进化规则来进行搜索计算和问题求解。遗传算法具有通用、并行、稳健、简单与全局优化能力强等突出优点，适用于解决复杂、困难的全局优化问题。遗产算法以其广泛的适应性渗透到研究与工程的各个领域，例如：组合优化、机器学习、自适应控制、规划设计和人工生命等，是21世纪有关智能计算中的重要技术之一。
文章的第一部分介绍了遗传算法的基本概念。第二部分介绍了遗传算法的原理。第三部分着重介绍具体实现，以及简单实例，主要体现遗传算法的实现过程。第四部分介绍了一个具体问题，如何用遗传算法来解决，以及实现时的一些基本问题。
文章在介绍遗传算法的原理以及各种运算的同时，还分析了一些应用中出现的基本问题，对于我们的解题实践有一定的指导意义。
关键词：遗传算法，遗传，群体
Genetic algorithm and its application Abstract: genetic algorithm genetic algorithms (GA) is a kind of reference biology natural selection and genetic mechanism of random search algorithm, is very suitable for the complex and non-linear problems that are difficult for traditional search methods. Genetic algorithm is a new subject based on Darwin's theory of evolution, which is developed on the computer simulation of life evolution. It is based on the survival of the fittest, the survival of the fittest natural evolution rule to search algorithm and solve problems. Genetic algorithm has the advantages of general, parallel, robust, simple and global optimization, which is suitable for solving complex and difficult global optimization problems. Inheritance algorithm with its extensive adaptability penetrated into various fields of research and engineering, for example: combinatorial optimization, machine learning, adaptive control, planning and design and artificial life, is one of the most important technologies in the 21st century the intelligent calculation.
The first part of the article introduces the basic concepts of genetic algorithm. The second part introduces the principle of genetic algorithm and three kinds of operations: selection, exchange, variation. The third part focuses on the specific implementation of the three operations, as well as a simple example, the main embodiment of the genetic algorithm to achieve the process. In the fourth part, the two parts are introduced, which are all the problems of NP-, how to use genetic algorithms to solve the problems, and some basic problems in the implementation of the problem.
In the introduction of the principle of genetic algorithm and various operations, it also analyzes the basic problems that arise in some applications.
Key words: genetic algorithm, genetic variation, population
目录一．遗传算法的基本概念
二．简单的遗传算法
三．简单的遗传算法运算示例
四．遗传算法应用举例
五．结束语
