{"title":"用多种群遗传算法优化业务流程设计","authors":"Nadir Mahammed, S. Bennabi, Mahmoud Fahsi","doi":"10.1145/3405962.3405971","DOIUrl":null,"url":null,"abstract":"This article discusses a multi-objective business process optimization. The authors present an approach for an evolutionary combinatorial multi-objective optimization of business process designs with a specified genetic algorithm based on multiple populations. The results show that the optimization approach is capable of producing a satisfactory number of optimized designs alternatives.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"405 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Business Process Designs with a Multiple Population Genetic Algorithm\",\"authors\":\"Nadir Mahammed, S. Bennabi, Mahmoud Fahsi\",\"doi\":\"10.1145/3405962.3405971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article discusses a multi-objective business process optimization. The authors present an approach for an evolutionary combinatorial multi-objective optimization of business process designs with a specified genetic algorithm based on multiple populations. The results show that the optimization approach is capable of producing a satisfactory number of optimized designs alternatives.\",\"PeriodicalId\":247414,\"journal\":{\"name\":\"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics\",\"volume\":\"405 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3405962.3405971\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3405962.3405971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing Business Process Designs with a Multiple Population Genetic Algorithm
This article discusses a multi-objective business process optimization. The authors present an approach for an evolutionary combinatorial multi-objective optimization of business process designs with a specified genetic algorithm based on multiple populations. The results show that the optimization approach is capable of producing a satisfactory number of optimized designs alternatives.