{"title":"控制系统综合问题的二元变分遗传规划","authors":"A. Diveev, G. Balandina, S. Konstantinov","doi":"10.1109/FSKD.2017.8393051","DOIUrl":null,"url":null,"abstract":"The paper describes a novel numerical symbolic regression method. It's called complete binary variational genetic programming. We use it for synthesis of optimal control. This method performs better than genetic programming at crossover, reduces the search area and speeds up search algorithm by using small variations. The efficiency of the new method is proven on the given example of control system synthesis for mobile robot.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Binary variational genetic programming for the problem of synthesis of control system\",\"authors\":\"A. Diveev, G. Balandina, S. Konstantinov\",\"doi\":\"10.1109/FSKD.2017.8393051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes a novel numerical symbolic regression method. It's called complete binary variational genetic programming. We use it for synthesis of optimal control. This method performs better than genetic programming at crossover, reduces the search area and speeds up search algorithm by using small variations. The efficiency of the new method is proven on the given example of control system synthesis for mobile robot.\",\"PeriodicalId\":236093,\"journal\":{\"name\":\"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2017.8393051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2017.8393051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Binary variational genetic programming for the problem of synthesis of control system
The paper describes a novel numerical symbolic regression method. It's called complete binary variational genetic programming. We use it for synthesis of optimal control. This method performs better than genetic programming at crossover, reduces the search area and speeds up search algorithm by using small variations. The efficiency of the new method is proven on the given example of control system synthesis for mobile robot.
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