{"title":"进化算法简介","authors":"F. Streichert","doi":"10.1201/9781482268713-14","DOIUrl":null,"url":null,"abstract":"Evolutionary Algorithms (EA) consist of several heuristics, which are able to solve optimisation tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on whole populations of possible solutions for a given task. EAs are an approved set of heuristics, which are flexible to use and postulate only neglectible requirements on the optimisation task. As a practical application, technical trading rules found by the use of EA will be presented.","PeriodicalId":153733,"journal":{"name":"Evolutionary Computation 1","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Introduction to evolutionary algorithms\",\"authors\":\"F. Streichert\",\"doi\":\"10.1201/9781482268713-14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Evolutionary Algorithms (EA) consist of several heuristics, which are able to solve optimisation tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on whole populations of possible solutions for a given task. EAs are an approved set of heuristics, which are flexible to use and postulate only neglectible requirements on the optimisation task. As a practical application, technical trading rules found by the use of EA will be presented.\",\"PeriodicalId\":153733,\"journal\":{\"name\":\"Evolutionary Computation 1\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Evolutionary Computation 1\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/9781482268713-14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolutionary Computation 1","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/9781482268713-14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evolutionary Algorithms (EA) consist of several heuristics, which are able to solve optimisation tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on whole populations of possible solutions for a given task. EAs are an approved set of heuristics, which are flexible to use and postulate only neglectible requirements on the optimisation task. As a practical application, technical trading rules found by the use of EA will be presented.
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