O. N. Teixeira, Walter Avelino da Luz Lobato, C. Yasojima, F. Brito, A. Teixeira, R. C. L. Oliveira
{"title":"A new hybrid nature-inspired metaheuristic for problem solving based on the Social Interaction Genetic Algorithm employing Fuzzy Systems","authors":"O. N. Teixeira, Walter Avelino da Luz Lobato, C. Yasojima, F. Brito, A. Teixeira, R. C. L. Oliveira","doi":"10.1109/HIS.2010.5600030","DOIUrl":"https://doi.org/10.1109/HIS.2010.5600030","url":null,"abstract":"This paper has the purpose to present a new hybrid nature inspired metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma (PD), for example. In this way it is presented the Social Interaction Genetic Algorithm (SIGA), to establish the necessary basis for the application of fuzzy concepts to get the F-SIGA Algorithm. Besides that, it is also presented the structure of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its behavior's strategy, which could be binary or fuzzy. At least the F-SIGA approach is presented in details, including all its steps. And finally some results are presented to an instance of the Traveling Salesman Problem.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115275273","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}
{"title":"Search personalization in hyperlinked environments by relevance propagation and ant colony optimization","authors":"P. Krömer, V. Snás̃el, J. Platoš, S. Owais","doi":"10.1109/HIS.2010.5600017","DOIUrl":"https://doi.org/10.1109/HIS.2010.5600017","url":null,"abstract":"Personalization is a promising way of improvement of the search services in large document collections and on the Web. User modeling is in the core of many personalization efforts because accurate user model can provide essential information for user specific search adjustments and result set processing. In this paper, we propose and study user modeling technique based on click-through data, relevance propagation and ant colony optimization.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125097009","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}
{"title":"Efficient protein-ligand docking using sustainable evolutionary algorithms","authors":"Emrah Atilgan, Jianjun Hu","doi":"10.1145/1830483.1830521","DOIUrl":"https://doi.org/10.1145/1830483.1830521","url":null,"abstract":"AutoDock is a widely used automated protein docking program in structure-based drug-design. Different search algorithms such as simulated annealing, traditional genetic algorithm (GA) and Lamarckian genetic algorithm (LGA) are implemented in AutoDock. However, the docking performance of these algorithms is still limited by the local optima issue of simulated annealing or the premature convergence issue typical in traditional evolutionary algorithms (EA). Due to the stochastic nature of these search algorithms, users usually need to run multiple times to get reasonable docking results, which is time-consuming. We have developed a new docking program AutoDockX by applying a sustainable GA, Age-Layered Population Structure (ALPS) to the protein docking problem. We tested the docking performance over three different proteins (pr, cox and hsp90) with more than 20 candidate ligands for each protein. Our experiments showed that the sustainable GA based AutodockX achieved significantly better docking performance in terms of running time and robustness than all the existing search algorithms implemented in the latest version of AutoDock. AutodockX thus has unique advantages in large-scale virtual screening.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116449066","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}