基于变异、交叉和选择概率的自适应遗传算法

A. Damia, M. Esnaashari, Mohammadreza Parvizimosaed
{"title":"基于变异、交叉和选择概率的自适应遗传算法","authors":"A. Damia, M. Esnaashari, Mohammadreza Parvizimosaed","doi":"10.1109/ICWR51868.2021.9443124","DOIUrl":null,"url":null,"abstract":"The Genetic Algorithm (GA) is an explore technique used to solve issues in many different applications. The genetic algorithm has some parameters, including crossover probability, selection mechanism, and mutation probability. In GA, parameter adaptation is an important research topic. This paper proposes a Probabilistic Adaptive Genetic Algorithm in which the mutation and crossover probabilities, as well as the selection mechanism are dynamically adapted throughout the running of the algorithm. A new set of rates is generated for the next iteration based on the differences between fitness values and individual, enhancing the searching global optimum exploitation. We have compared the proposed algorithm with some common and state-of-the-art adaptive strategies such as dynamic adaptive, dynamic deterministic, dynamic self-adaptive, and static on a set of several functions with varying degrees of complexity. Experimental results on several popular test functions have shown that the results of the proposed algorithm are significantly better than these methods on both convergence speed and the solutions' quality.The reason that the proposed method has better results than other methods is the adaptation of each parameter of the genetic algorithm.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Adaptive Genetic Algorithm Based on Mutation and Crossover and Selection Probabilities\",\"authors\":\"A. Damia, M. Esnaashari, Mohammadreza Parvizimosaed\",\"doi\":\"10.1109/ICWR51868.2021.9443124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Genetic Algorithm (GA) is an explore technique used to solve issues in many different applications. The genetic algorithm has some parameters, including crossover probability, selection mechanism, and mutation probability. In GA, parameter adaptation is an important research topic. This paper proposes a Probabilistic Adaptive Genetic Algorithm in which the mutation and crossover probabilities, as well as the selection mechanism are dynamically adapted throughout the running of the algorithm. A new set of rates is generated for the next iteration based on the differences between fitness values and individual, enhancing the searching global optimum exploitation. We have compared the proposed algorithm with some common and state-of-the-art adaptive strategies such as dynamic adaptive, dynamic deterministic, dynamic self-adaptive, and static on a set of several functions with varying degrees of complexity. Experimental results on several popular test functions have shown that the results of the proposed algorithm are significantly better than these methods on both convergence speed and the solutions' quality.The reason that the proposed method has better results than other methods is the adaptation of each parameter of the genetic algorithm.\",\"PeriodicalId\":377597,\"journal\":{\"name\":\"2021 7th International Conference on Web Research (ICWR)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 7th International Conference on Web Research (ICWR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICWR51868.2021.9443124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 7th International Conference on Web Research (ICWR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWR51868.2021.9443124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

遗传算法(GA)是一种用于解决许多不同应用中的问题的探索技术。遗传算法具有交叉概率、选择机制和突变概率等参数。在遗传算法中,参数自适应是一个重要的研究课题。本文提出了一种概率自适应遗传算法,该算法在整个算法运行过程中动态地适应变异和交叉概率以及选择机制。基于适应度值和个体之间的差异,为下一次迭代生成一组新的速率,增强了搜索全局最优开发的能力。我们将所提出的算法与一些常见的和最先进的自适应策略(如动态自适应、动态确定性、动态自适应和静态)进行了比较,这些策略针对一组具有不同复杂程度的函数。在几种常用的测试函数上的实验结果表明,该算法在收敛速度和解的质量上都明显优于这些方法。该方法优于其他方法的原因在于对遗传算法的各个参数进行了自适应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Genetic Algorithm Based on Mutation and Crossover and Selection Probabilities
The Genetic Algorithm (GA) is an explore technique used to solve issues in many different applications. The genetic algorithm has some parameters, including crossover probability, selection mechanism, and mutation probability. In GA, parameter adaptation is an important research topic. This paper proposes a Probabilistic Adaptive Genetic Algorithm in which the mutation and crossover probabilities, as well as the selection mechanism are dynamically adapted throughout the running of the algorithm. A new set of rates is generated for the next iteration based on the differences between fitness values and individual, enhancing the searching global optimum exploitation. We have compared the proposed algorithm with some common and state-of-the-art adaptive strategies such as dynamic adaptive, dynamic deterministic, dynamic self-adaptive, and static on a set of several functions with varying degrees of complexity. Experimental results on several popular test functions have shown that the results of the proposed algorithm are significantly better than these methods on both convergence speed and the solutions' quality.The reason that the proposed method has better results than other methods is the adaptation of each parameter of the genetic algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信