{"title":"技术交易规则协同演化方法的实证研究","authors":"K. Adamu, S. Phelps","doi":"10.1109/UKCI.2010.5625600","DOIUrl":null,"url":null,"abstract":"Coevolutionary algorithms employ collaboration methods in assessing the fitness of solutions. In this paper, we explore four different collaboration methods for coevolving technical trading rules for entering, and exiting long and short positions, and stop loss rules for long and short positions respectively. Our results show that our problem is sensitive to the collaboration method being used and that an averaging method with more than one collaborator from each species is most efficient for our problem.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An empirical study of collaboration methods for coevolving technical trading rules\",\"authors\":\"K. Adamu, S. Phelps\",\"doi\":\"10.1109/UKCI.2010.5625600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coevolutionary algorithms employ collaboration methods in assessing the fitness of solutions. In this paper, we explore four different collaboration methods for coevolving technical trading rules for entering, and exiting long and short positions, and stop loss rules for long and short positions respectively. Our results show that our problem is sensitive to the collaboration method being used and that an averaging method with more than one collaborator from each species is most efficient for our problem.\",\"PeriodicalId\":403291,\"journal\":{\"name\":\"2010 UK Workshop on Computational Intelligence (UKCI)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 UK Workshop on Computational Intelligence (UKCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UKCI.2010.5625600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 UK Workshop on Computational Intelligence (UKCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UKCI.2010.5625600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An empirical study of collaboration methods for coevolving technical trading rules
Coevolutionary algorithms employ collaboration methods in assessing the fitness of solutions. In this paper, we explore four different collaboration methods for coevolving technical trading rules for entering, and exiting long and short positions, and stop loss rules for long and short positions respectively. Our results show that our problem is sensitive to the collaboration method being used and that an averaging method with more than one collaborator from each species is most efficient for our problem.