{"title":"一种利用进化计算技术内部推理求解csp的新方案","authors":"M. Ionita, Mihaela Breaban, Cornelius Croitoru","doi":"10.1109/SYNASC.2006.7","DOIUrl":null,"url":null,"abstract":"Combining inference and search produces successful schemes for solving constraint satisfaction problems. Based on this idea a general scheme which uses inference inside evolutionary computation techniques is presented. A genetic algorithm and the particle swarm optimization heuristic make use of adaptable inference levels offered by the mini-bucket elimination algorithm. Experimental results prove the efficiency of our approach in solving the Max-CSP optimization task. The inference/search trade-off is analyzed","PeriodicalId":309740,"journal":{"name":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Scheme of Using Inference Inside Evolutionary Computation Techniques to Solve CSPs\",\"authors\":\"M. Ionita, Mihaela Breaban, Cornelius Croitoru\",\"doi\":\"10.1109/SYNASC.2006.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Combining inference and search produces successful schemes for solving constraint satisfaction problems. Based on this idea a general scheme which uses inference inside evolutionary computation techniques is presented. A genetic algorithm and the particle swarm optimization heuristic make use of adaptable inference levels offered by the mini-bucket elimination algorithm. Experimental results prove the efficiency of our approach in solving the Max-CSP optimization task. The inference/search trade-off is analyzed\",\"PeriodicalId\":309740,\"journal\":{\"name\":\"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYNASC.2006.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2006.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Scheme of Using Inference Inside Evolutionary Computation Techniques to Solve CSPs
Combining inference and search produces successful schemes for solving constraint satisfaction problems. Based on this idea a general scheme which uses inference inside evolutionary computation techniques is presented. A genetic algorithm and the particle swarm optimization heuristic make use of adaptable inference levels offered by the mini-bucket elimination algorithm. Experimental results prove the efficiency of our approach in solving the Max-CSP optimization task. The inference/search trade-off is analyzed
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