{"title":"遗传算法和进化计算","authors":"Ningchuan Xiao, Marc Armstrong","doi":"10.22224/gistbok/2020.1.1","DOIUrl":null,"url":null,"abstract":"A genetic algorithm is a technique for optimization; that is, it can be used to find the minimum or maximum of some arbitrary function. While there are a lar ge number of mathematical techniques for accomplishing this, both in general and for specific circumstances, a genetic algorithm is unique. It is a stochastic method, and it will find a global minimum, neither property being singular . The approach is remarkable because it is based on the way that a population of living or ganisms grows and evolves, fitting into their ecological niche better with each generation.","PeriodicalId":325401,"journal":{"name":"Geographic Information Science & Technology Body of Knowledge","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Genetic Algorithms and Evolutionary Computing\",\"authors\":\"Ningchuan Xiao, Marc Armstrong\",\"doi\":\"10.22224/gistbok/2020.1.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A genetic algorithm is a technique for optimization; that is, it can be used to find the minimum or maximum of some arbitrary function. While there are a lar ge number of mathematical techniques for accomplishing this, both in general and for specific circumstances, a genetic algorithm is unique. It is a stochastic method, and it will find a global minimum, neither property being singular . The approach is remarkable because it is based on the way that a population of living or ganisms grows and evolves, fitting into their ecological niche better with each generation.\",\"PeriodicalId\":325401,\"journal\":{\"name\":\"Geographic Information Science & Technology Body of Knowledge\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geographic Information Science & Technology Body of Knowledge\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22224/gistbok/2020.1.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geographic Information Science & Technology Body of Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22224/gistbok/2020.1.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A genetic algorithm is a technique for optimization; that is, it can be used to find the minimum or maximum of some arbitrary function. While there are a lar ge number of mathematical techniques for accomplishing this, both in general and for specific circumstances, a genetic algorithm is unique. It is a stochastic method, and it will find a global minimum, neither property being singular . The approach is remarkable because it is based on the way that a population of living or ganisms grows and evolves, fitting into their ecological niche better with each generation.
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