求解可满足性问题的两种进化方法的实证比较

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI:10.1109/ICEC.1994.349908

Jin-Kao Hao, R. Dorne

{"title":"求解可满足性问题的两种进化方法的实证比较","authors":"Jin-Kao Hao, R. Dorne","doi":"10.1109/ICEC.1994.349908","DOIUrl":null,"url":null,"abstract":"The paper compares two evolutionary methods for model finding in the satisfiability problem (SAT): genetic algorithms (GAs) and the mask method (MASK). The main characteristics of these two methods are that both of them are population-based, and use binary representation. Great care is taken to make sure that the same SAT instances and the same criteria are used in the comparison. Results indicate that MASK greatly outperforms GAs in the sense that MASK manages to deal with harder SAT instances at a lower cost.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"178 9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"An empirical comparison of two evolutionary methods for satisfiability problems\",\"authors\":\"Jin-Kao Hao, R. Dorne\",\"doi\":\"10.1109/ICEC.1994.349908\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper compares two evolutionary methods for model finding in the satisfiability problem (SAT): genetic algorithms (GAs) and the mask method (MASK). The main characteristics of these two methods are that both of them are population-based, and use binary representation. Great care is taken to make sure that the same SAT instances and the same criteria are used in the comparison. Results indicate that MASK greatly outperforms GAs in the sense that MASK manages to deal with harder SAT instances at a lower cost.<<ETX>>\",\"PeriodicalId\":393865,\"journal\":{\"name\":\"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence\",\"volume\":\"178 9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEC.1994.349908\",\"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 First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEC.1994.349908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 17

摘要

本文比较了两种用于求解可满足性问题模型的进化方法:遗传算法和掩码法。这两种方法的主要特点是它们都是基于种群的，并且使用二进制表示。我们非常小心地确保在比较中使用相同的SAT实例和相同的标准。结果表明，MASK在某种意义上大大优于GAs，因为MASK能够以更低的成本处理更难的SAT实例

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

An empirical comparison of two evolutionary methods for satisfiability problems

The paper compares two evolutionary methods for model finding in the satisfiability problem (SAT): genetic algorithms (GAs) and the mask method (MASK). The main characteristics of these two methods are that both of them are population-based, and use binary representation. Great care is taken to make sure that the same SAT instances and the same criteria are used in the comparison. Results indicate that MASK greatly outperforms GAs in the sense that MASK manages to deal with harder SAT instances at a lower cost.<>

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence

自引率

0.00%

发文量