2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems最新文献_第2页

A Method to Control Parameters of Evolutionary Algorithms by Using Reinforcement Learning 基于强化学习的进化算法参数控制方法

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.22

Yoshitaka Sakurai, K. Takada, Takashi Kawabe, S. Tsuruta

{"title":"A Method to Control Parameters of Evolutionary Algorithms by Using Reinforcement Learning","authors":"Yoshitaka Sakurai, K. Takada, Takashi Kawabe, S. Tsuruta","doi":"10.1109/SITIS.2010.22","DOIUrl":"https://doi.org/10.1109/SITIS.2010.22","url":null,"abstract":"A search method using an evolutionary algorithm such as a genetic algorithm (GA) is very effective if the parameter is appropriately set. However, the optimum parameter setting was so difficult that each optimal method depending on each problem pattern must be developed one by one. Therefore, this has required special expertise and large amounts of verification experiment. In order to solve this problem, a new method called gadaptive parameter controlh is proposed, which adaptively controls parameters of an evolutionary algorithm. However, since this method just increases the selection probability of a search operator that generated a well evaluated individual, this is apt to be a shortsighted optimization method. On the contrary, a method is proposed to realize longsighted optimal parameter control of GA using reinforcement learning. However, this method does neither consider the calculation cost of search operators nor multipoint search characteristics of GA. This paper proposes a method to efficiently control parameters of an evolutionary algorithm by using the reinforcement learning where the reward decision rules are elaborately incorporated under the consideration of GAfs multipoint search characteristics and calculation cost of the search operator. It is expected that this method can efficiently learn parameters to optimally select search operators of GA for approximately solving Travelling Salesman Problems (TSPs).","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114358298","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}

引用次数: 43

Fractal-based Impressionist Style Rendering 基于分形的印象派风格渲染

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.20

Farès Belhadj, V. Boyer

引用次数: 3

Native XML Document Fragmentation Model 原生XML文档碎片模型

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.47

Leykun Birhanu, Solomon Atnafu, Fekade Getahun Taddesse

引用次数: 9

Integrating Ontologies in Database Scheme: Ontology-Based Views Conceptual Modeling 数据库方案中本体的集成:基于本体的视图概念建模

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.52

Oumar Sy, Moussa Lo, Denio Duarte

引用次数: 2

Can Mean Shift Trackers Perform Better? Mean Shift追踪器能表现得更好吗?

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.26

Huiyu Zhou, G. Schaefer, Yuan Yuan, M. E. Celebi

引用次数: 1

Knowledge Cartography and Social Network Representation: Application to Collaborative Platforms in Scientific Area 知识制图与社会网络表示:在科学领域协作平台中的应用

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.45

Michel Plantié, Pierre-Michel Riccio

引用次数: 3

Protein Structure Modeling in Two-Level Topology Strings for Structure Comparison 用于结构比较的两级拓扑串中的蛋白质结构建模

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.57

J. Razmara, S. Deris, Sepideh Parvizpour

引用次数: 0

A Formal Approach for Interoperability Testing of Security Rules 安全规则互操作性测试的形式化方法

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.53

Mazen El Maarabani, Iksoon Hwang, A. Cavalli

引用次数: 9

Automatic Segmentation of Abdominal Aortic Aneurysm in Computed Tomography Images Using Spatial Fuzzy C-Means 基于空间模糊c均值的腹主动脉瘤ct图像自动分割

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.38

Emadaldin Mozafari Majd, U. U. Sheikh, S. Abu-Bakar

引用次数: 6

Detecting Worms Using Data Mining Techniques: Learning in the Presence of Class Noise 使用数据挖掘技术检测蠕虫:在班级噪声存在下学习

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems Pub Date : 2010-12-15 DOI: 10.1109/SITIS.2010.41

I. Ismail, M. N. Marsono, S. Nor

{"title":"Detecting Worms Using Data Mining Techniques: Learning in the Presence of Class Noise","authors":"I. Ismail, M. N. Marsono, S. Nor","doi":"10.1109/SITIS.2010.41","DOIUrl":"https://doi.org/10.1109/SITIS.2010.41","url":null,"abstract":"Worms are self-contained programs that spread over the Internet. Worms cause problems such as lost of information, information theft and denial-of-service attacks. The first part of the paper evaluates the detection of worms based on content classification by using all machine learning techniques available in WEKA data mining tools. Four most accurate and quite fast classifiers are identified for further analysisâ€“Naive Bayes, J48, SMO and Winnow. Results show that classification using machine learning techniques could classify worms to 99% accuracy. From the accuracy perspective, J48 performs better than other algorithms meanwhile Naive Bayes and Winnow show the best performances in terms of speed. The second part of the paper analyzes the accuracy these four classifiers under the presence of class noise in learning corpora. By injecting class noise ranging between 0% and 50% into positive and negative corpora, results from the simulation show gradual decrease in accuracy and increase in false positive and false negative for all analyzed techniques. The presence of the classes noise affects false positive more significantly compared to false negative. The results show that worm detection with classification algorithms could not tolerate the presence of classes noise in learning corpora.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132133152","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}

引用次数: 16