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所在平台: Udemy |
课程主页: https://www.udemy.com/course/genetic-algorithm-concepts-and-working/
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课程名称：遗传算法概念与工作原理 课程概述：遗传算法是一种基于搜索的优化算法，用于解决传统方法无法解决的问题。这是一种随机化算法，每一步都遵循随机原理。遗传算法由密歇根大学的约翰·霍兰德在1960年开发，他的提议基于查尔斯·达尔文的生物进化理论。遗传算法遵循“优胜劣汰”的原则，只有最强壮的个体才能存活到下一代，随着世代的演化，只有最适合的个体才能生存。遗传算法作用于解决方案，因此称为基于搜索的优化算法。它从现有的解决方案集中搜索最优解。遗传算法的过程如下： 1. 从现有人口中随机选择一些个体（解决方案） 2. 计算适应度函数 3. 选择最适合的个体作为父代染色体 4. 执行交叉（重组） 5. 执行变异 6. 重复此过程直到满足终止条件 以上步骤表明，遗传算法是一种随机化的基于搜索的优化算法。 本课程分为四个模块： 第一模块 - 遗传算法的介绍、历史及相关术语 第二模块 - 遗传算法的工作原理及示例 第三模块 - 编码类型、选择方法、交叉与变异方法 第四模块 - 遗传算法的编码与应用 祝您学习愉快！
Genetic Algorithm is a search based optimization algorithm used to solve problems were traditional methods fails. It is an randomized algorithm where each step follows randomization principle.Genetic Algorithm was developed by John Holland, from the University of Michigan, in 1960. He proposed this algorithm based on the Charles Darwin's theory on Evolution of organism. Genetic Algorithm follows the principal of "Survival of Fittest". Only the fittest individual has the possibility to survive to the next generation and hence when the generations evolve only the fittest individuals survive.Genetic Algorithms operates on Solutions, hence called as search based optimization algorithm. It search for an optimal solution from the existing set of solutions in search space. The process of Genetic Algorithm is given as,1. Randomly choose some individuals (Solutions) from the existing population2. Calculate the fitness function3. Choose the fittest individuals as parental chromosomes4. Perform crossover (Recombination)5. Perform Mutation6. Repeat this process until the termination conditionThis steps indicated that Genetic Algorithm is an Randomized, search based optimization Algorithm.This course is divided into four modules.First module - Introduction, history and terminologies used in Genetic Algorithm.Second Module - Working of genetic algorithm with an exampleThird Module - Types of Encoding, Selection, Crossover and Mutation methodsFourth module - Coding and Applications of Genetic AlgorithmHappy Learning!!!
