In this paper, the time-dependent travelling salesman problem (TDTSP) is reviewed and the heuristic based on ant colony optimization for solving the TDTSP is proposed. The TDTSP is an extension of the classical travelling salesman problem in which the edge costs depend on the order in which the edges are visited. This extension is even more computationally complex than the original problem and therefore a heuristic must be used in order to get a solution close to the optimal one for larger-scale problems. We combine the ant colony optimization algorithm with a modified local search and apply the heuristic to a simplified version of the flying tourist problem.
{"title":"Ant Colony Optimization for Time-Dependent Travelling Salesman Problem","authors":"Petra Tomanová, Vladimír Holý","doi":"10.1145/3396474.3396485","DOIUrl":"https://doi.org/10.1145/3396474.3396485","url":null,"abstract":"In this paper, the time-dependent travelling salesman problem (TDTSP) is reviewed and the heuristic based on ant colony optimization for solving the TDTSP is proposed. The TDTSP is an extension of the classical travelling salesman problem in which the edge costs depend on the order in which the edges are visited. This extension is even more computationally complex than the original problem and therefore a heuristic must be used in order to get a solution close to the optimal one for larger-scale problems. We combine the ant colony optimization algorithm with a modified local search and apply the heuristic to a simplified version of the flying tourist problem.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123739763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexandr Grichshenko, Luiz Jonatã Pires de Araújo, Susanna Gimaeva, J. A. Brown
Tabu Search (TS) metaheuristic improves simple local search algorithms (e.g. steepest ascend hill-climbing) by enabling the algorithm to escape local optima points. It has shown to be useful for addressing several combinatorial optimization problems. This paper investigates the performance of TS and considers the effects of the size of the Tabu list and the size of the neighbourhood for a procedural content generation, specifically the generation of maps for a popular tabletop game called Terra Mystica. The results validate the feasibility of the proposed method and how it can be used to generate maps that improve existing maps for the game.
{"title":"Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game","authors":"Alexandr Grichshenko, Luiz Jonatã Pires de Araújo, Susanna Gimaeva, J. A. Brown","doi":"10.1145/3396474.3396492","DOIUrl":"https://doi.org/10.1145/3396474.3396492","url":null,"abstract":"Tabu Search (TS) metaheuristic improves simple local search algorithms (e.g. steepest ascend hill-climbing) by enabling the algorithm to escape local optima points. It has shown to be useful for addressing several combinatorial optimization problems. This paper investigates the performance of TS and considers the effects of the size of the Tabu list and the size of the neighbourhood for a procedural content generation, specifically the generation of maps for a popular tabletop game called Terra Mystica. The results validate the feasibility of the proposed method and how it can be used to generate maps that improve existing maps for the game.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127323239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
No single machine learning algorithm is most accurate for all problems due to the effect of an algorithm's inductive bias. Research has shown that a combination of experts of the same type, referred to as a mixture of homogeneous experts, can increase the accuracy of ensembles by reducing the adverse effect of an algorithm's inductive bias. However, the predictive power of a mixture of homogeneous experts is still limited by the inductive bias of the algorithm that makes up the mixture. For this reason, combinations of different machine learning algorithms, referred to as a mixture of heterogeneous experts, has been proposed to take advantage of the strengths of different machine learning algorithms and to reduce the adverse effects of the inductive biases of these algorithms. This paper presents a mixture of heterogeneous experts, and evaluates its performance to that of a number of mixtures of homogeneous experts on a set of classification problems. The results indicate that a mixture of heterogeneous experts aggregates the advantages of experts, increasing the accuracy of predictions. The mixture of heterogeneous experts not only outperformed all homogeneous ensembles on two of the datasets, but also achieved the best overall accuracy rank across the various datasets.
{"title":"Mixtures of Heterogeneous Experts","authors":"Callum Parton, A. Engelbrecht","doi":"10.1145/3396474.3396484","DOIUrl":"https://doi.org/10.1145/3396474.3396484","url":null,"abstract":"No single machine learning algorithm is most accurate for all problems due to the effect of an algorithm's inductive bias. Research has shown that a combination of experts of the same type, referred to as a mixture of homogeneous experts, can increase the accuracy of ensembles by reducing the adverse effect of an algorithm's inductive bias. However, the predictive power of a mixture of homogeneous experts is still limited by the inductive bias of the algorithm that makes up the mixture. For this reason, combinations of different machine learning algorithms, referred to as a mixture of heterogeneous experts, has been proposed to take advantage of the strengths of different machine learning algorithms and to reduce the adverse effects of the inductive biases of these algorithms. This paper presents a mixture of heterogeneous experts, and evaluates its performance to that of a number of mixtures of homogeneous experts on a set of classification problems. The results indicate that a mixture of heterogeneous experts aggregates the advantages of experts, increasing the accuracy of predictions. The mixture of heterogeneous experts not only outperformed all homogeneous ensembles on two of the datasets, but also achieved the best overall accuracy rank across the various datasets.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"17 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125796973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling unstructured data have also received increasing attention from the research community. The paper deals with the problem of Association Rule Text Mining. To solve the problem, the PSO-ARTM method was proposed, that consists of three steps: Text preprocessing, Association Rule Text Mining using population-based metaheuristics, and text postprocessing. The method was applied to a transaction database obtained from professional triathlon athletes' blogs and news posted on their websites. The obtained results reveal that the proposed method is suitable for Association Rule Text Mining and, therefore, offers a promising way for further development.
{"title":"Population-based metaheuristics for Association Rule Text Mining","authors":"Iztok Fister, S. Deb, Iztok Fister","doi":"10.1145/3396474.3396493","DOIUrl":"https://doi.org/10.1145/3396474.3396493","url":null,"abstract":"Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling unstructured data have also received increasing attention from the research community. The paper deals with the problem of Association Rule Text Mining. To solve the problem, the PSO-ARTM method was proposed, that consists of three steps: Text preprocessing, Association Rule Text Mining using population-based metaheuristics, and text postprocessing. The method was applied to a transaction database obtained from professional triathlon athletes' blogs and news posted on their websites. The obtained results reveal that the proposed method is suitable for Association Rule Text Mining and, therefore, offers a promising way for further development.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：