A crossword puzzle generator using genetic algorithms with Wisdom of Artificial Crowds

Douglas Bonomo, Adrian P. Lauf, Roman V. Yampolskiy
{"title":"A crossword puzzle generator using genetic algorithms with Wisdom of Artificial Crowds","authors":"Douglas Bonomo, Adrian P. Lauf, Roman V. Yampolskiy","doi":"10.1109/CGames.2015.7272960","DOIUrl":null,"url":null,"abstract":"NP-hard problems, such as generating crossword puzzles, are candidates for solution by genetic algorithms (GAs). A combination of both a genetic algorithms and a Wisdom of Artificial Crowds (WoAC) aggregation method was developed for the purpose of creating crossword puzzles given a particular outline. The program was written using a GPL Hunspell wrapper NHunspell for word verification and suggestion. In American-style crossword puzzles, we saw performance improvements of around 20% in reaching a local optima, with a 6% improvement in the number of successful words found by a GA + WoAC algorithm over the baseline genetic algorithm.","PeriodicalId":447614,"journal":{"name":"2015 Computer Games: AI, Animation, Mobile, Multimedia, Educational and Serious Games (CGAMES)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Computer Games: AI, Animation, Mobile, Multimedia, Educational and Serious Games (CGAMES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CGames.2015.7272960","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

NP-hard problems, such as generating crossword puzzles, are candidates for solution by genetic algorithms (GAs). A combination of both a genetic algorithms and a Wisdom of Artificial Crowds (WoAC) aggregation method was developed for the purpose of creating crossword puzzles given a particular outline. The program was written using a GPL Hunspell wrapper NHunspell for word verification and suggestion. In American-style crossword puzzles, we saw performance improvements of around 20% in reaching a local optima, with a 6% improvement in the number of successful words found by a GA + WoAC algorithm over the baseline genetic algorithm.
一个使用遗传算法和人工群体智慧的填字游戏生成器
np困难的问题,如生成填字游戏,是遗传算法(GAs)的候选解决方案。结合遗传算法和人工群体智慧(WoAC)聚合方法,开发了一种用于创建给定特定轮廓的填字游戏的组合方法。该程序是使用GPL Hunspell包装器NHunspell编写的,用于单词验证和建议。在美式填字游戏中,我们发现在达到局部最优时,GA + WoAC算法的性能提高了约20%,与基线遗传算法相比,GA + WoAC算法找到的成功单词数量提高了6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信