检索结果-内蒙古大学图书馆

Wavelet neural networks optimized by QPSO for network anomaly detection

Journal of Computational Information systems 2011年第7期7卷 2452-2460页

作者： Liu, Yuan Ma, Ruhui School of Digital Media Jiangnan University Wuxi 214122 China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer science and Engineering Shanghai Jiaotong University Shanghai 200240 China

In this paper, a novel hybrid algorithm based wavelet neural network (WNN) is proposed for network anomaly detection. This new evolutionary algorithm, which is based on a hybrid of Quantum-Behaved Particle Swarm Optimization (QPSO) and conjugate gradient algorithm (CG), is employed to train WNN. The Quantum-Behaved Particle Swarm Optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. Due to the particles in the multi-dimensional space seeking the best position so quickly, it would result in the dangerous of stagnation, which would make the QPSO impossible to arrive at the global optimum. In order to overcome defects of QPSO, the improved hybrid algorithm was proposed. Experimental result on KDD 99 intrusion detection datasets shows that this WNN using the novel hybrid algorithm has high detection rate while maintaining a low false positive rate. Copyright © 2011 Binary Information Press.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Using co-evolved RTS opponents to teach spatial tactics

Using co-evolved RTS opponents to teach spatial tactics

引用

IEEE Symposium on Computational Intelligence and Games, CIG

作者： Greg Smith Phillipa Avery Ramona Houmanfar Sushil Louis Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering University of Nevada Reno NV USA

This paper describes a co-evolutionary algorithm for generating simple spatially oriented tactics and considers whether students can learn better by playing against co-evolved opponents or by playing against an expert system or other similar hard-coded opponent. Although a number of artificially intelligent tutoring and e-learning systems exist, our work looks at using co-evolution to generate competent opponents for human students to learn from. This paper describes and discusses early results on using computationally intelligent opponents for tactical training of human students. Initial results indicate that the learning environment for human players, measured by game difficulty and transfer of training, is comparable across co-evolved and hard-coded computer opponents.

关键词： Games Boats Training Humans computers Artificial intelligence Biological cells

来源：评论

学校读者我要写书评

暂无评论

Spectral and semidefinite relaxations of the CLUHSIC algorithm

Spectral and semidefinite relaxations of the CLUHSIC algorit...

引用

10th SIAM International Conference on Data Mining, SDM 2010

作者： Yang, Wen-Yun Kwok, James T. Lu, Bao-Liang University of California Los Angeles CA United States Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai China

CLUHSIC is a recent clustering framework that unifies the geometric, spectral and statistical views of clustering. In this paper, we show that the recently proposed discriminative view of clustering, which includes the DIFFRAC and DisKmeans algorithms, can also be unified under the CLUH-SIC framework. Moreover, CLUHSIC involves integer programming and one has to resort to heuristics such as iterative local optimization. In this paper, we propose two relaxations that are much more disciplined. The first one uses spectral techniques while the second one is based on semidefinite programming (SDP). Experimental results on a number of structured clustering tasks show that the proposed method significantly outperforms existing optimization methods for CLUHSIC. Moreover, it can also be used in semi-supervised classification. Experiments on real-world protein subcellu-lar localization data sets clearly demonstrate the ability of CLUHSIC in incorporating structural and evolutionary information. Copyright © by SIAM.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

Evolving coordinated spatial tactics for autonomous entities using influence maps

Evolving coordinated spatial tactics for autonomous entities...

引用

IEEE Symposium on Computational Intelligence and Games, CIG

作者： Phillipa Avery Sushil Louis Benjamin Avery Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering University of Nevada Reno NV USA Evolutionary Computing Systems Laboratory Dept. of Computer Science and Engineering University of Nevada Reno USA

We evolve tactical control for entity groups in a naval real-time strategy game. Since tactical maneuvering involves spatial reasoning, our evolutionary algorithm evolves a set of influence maps that help specify an entity's spatial objectives. The entity then uses the A* route finding algorithm to generate waypoints according to the influence map, and follows them to achieve spatial objectives. Using this representation, our evolutionary algorithm quickly evolves increasingly better capture-the-flag tactics on three increasingly difficult maps. These preliminary results indicate (1) the usefulness of our particular influence map encoding for representing spatially resolved tactics and (2) the potential for using co-evolution to generate increasingly complex and competent tactics in our game. More generally, this work represents another step in our ongoing effort to investigate the co-evolution of competent game players in a real-time, continuous, environment that does not assume complete knowledge of the game state.

关键词： Boats evolutionary computation Strategic planning Competitive intelligence Distributed control Automatic control Spatial resolution Encoding Application software computer applications

来源：评论

学校读者我要写书评

暂无评论

Abstracts from the 2011 Joint Annual Meeting of the Society for Northwestern Vertebrate Biology and Washington Chapter of the Wildlife Society, Held at the Wesley Inn, Gig Harbor, Washington, March 23–25, 2011

引用

Northwestern Naturalist 2011年第2期92卷 136-166页

作者： Michael J. Adams Christopher A. Pearl Jessica Y. Adkins Daniel D. Roby Ladd Bayliss Peter J. Loschl Donald E. Lyons Yasuko Suzuki Hannah E. Anderson Sarah T. Hamman Keith B. Aubry Catherine M. Raley John J. Rohrer Eric C. Lofroth Scott H. Fitkin Michael L. Best Hartwell H. Welsh Jamie B. Bettaso Don T. Ashton Hartwell H. Welsh Scott McBain Lindsay M. Biga Andrew R. Blaustein Susan F. Brady R. Steven Wagner Daniel J. Selski James E. Johnson R. Bruce Bury Wynn W. Cudmore Hartwell H. Welsh Nathan D. Chelgren Tara Chestnut Chaucney W. Anderson Julie Kirshtein Megan T. Cook Tiffany S. Garcia Stephen Corn Erin Muths Amanda Kissel Rick Scherer Eric Delvin Jonathan D. Bakker Peter W. Dunwiddie Diana L. Dishman Deborah A. Duffield Harriet R. Huber Dyanna M. Lambourn Karen M. Dvornich Greg Falxa Gary M. Fellers Patrick M. Kleeman David Miller Brandon Fessler Jason T. Irwin Daniel D. Beck R. Steven Wagner Marc P. Hayes John A. Frew Christian E. Grue Chris R. Friesen Robert T. Mason Stevan J. Arnold Suzanne Estes Tiffany S. Garcia Katie M. Dugger Josh Johnson Mike Rochelle A. J. Kroll Stephanie S. Gervasi Caren S. Goldberg Dan Davis Erica Bree Rosenblum Bill Bosworth Lisette P. Waits Purnima Govindarajulu Gregory A. Green Steve Negri Jennifer R. Griffiths Daniel E. Schindler Christian E. Grue Karen M. Dvornich Adam G. Hansen David A. Beauchamp Casey M. Baldwin Marc P. Hayes Gretchen Padgett-Flohr Blake R. Hossack Winsor H. Lowe Paul Stephen Corn Aaron N. Johnston Kris Kendell Dave Prescott Scott Stevens Peter M. Kiffney Carol Volk Kerensa A. King Christian E. Grue James M. Grassley Andrew J. Kroll Matt Hane Josh Johnston Mike Rochelle Stuart Farber Niels Leuthold Michael J. Adams John P. Hayes Bo Li Michael T. Brett Kent B. Livezey Ilon E. Logan Teresa J. Lorenz Kimberly A. Sullivan Amanda V. Bakian Deanna Lynch Nancy Thomas Krysten Schuler Steven Jeffries Joshua Malt Kelly R. McAllister Joanne P. Schuett-Hames Aimee P. McIntyre Jay E. Jones Eric M. Lund Frithiof T. Waterstrat Jack N. Giovanini Stephen D. Duke Marc P. Hayes US Forest Service Pacific Northwest Research Station Olympia WA 98512 kaubry@fs.fed.us US Forest Service Okanogan-Wenatchee National Forest Methow Valley Ranger District Winthrop WA 98862 British Columbia Ministry of Environment Victoria BC V8W9M1 Washington Department of Fish and Wildlife Winthrop WA 98862 Humboldt State University 1 Harpst St. Arcata CA 95521 addsmike14@*** US Forest Service Pacific Southwest Research Station 1700 Bayside Dr. Arcata CA 95521 hwelsh@fs.fed.us US Fish and Wildlife Service Arcata Field Office 1655 Heindon Road Arcata CA 95521 jamie_bettaso@fws.gov Redwood Sciences Laboratory US Forest Service1700 Bayview Drive Arcata CA 99521 dashton@fs.fed.us hwelsh@fs.fed.us McBain &amp Trush 980 7th Street Arcata CA 95521 scott@*** Oregon State University Environmental Sciences Graduate Program Corvallis OR 97331 lindsay.biga@science.oregonstate.edu Oregon State University Department of Zoology Corvallis OR 97331 US Geological Survey Forest and Rangeland Ecosystem Science Center 3200 SW Jefferson Way Corvallis OR 97331 buryb@usgs.gov Life Science Department Chemeketa Community College Salem OR 97309 wynn.cudmore@chemeketa.edu US Forest Service Redwood Sciences Laboratory 1700 Bayview Drive Arcata CA 95521 hwelsh@fs.fed.us USGS Oregon Water Science Center 2130 SW 5th Avenue Portland OR 97207 chestnut@usgs.gov USGS Headquarters 12201 Sunrise Valley Drive Reston VA 20192 US Geological Survey Northern Rocky Mountain Science Center Aldo Leopold Wilderness Research Institute 790 E. Beckwith Avenue Missoula MT 59801 scorn@usgs.gov US Geological Survey Fort Collins Science Center 2150 Centre Avenue Building C Fort Collins CO 80526 Department of Biological Sciences Simon Fraser University Burnaby BC V5A 1S6 Colorado State University Department of Fishery and Wildlife Biology Fort Collins CO 80523 The Nature Conservancy 120 Union Ave SE Olympia WA 98501 edelvin@tnc.org School of Forest Resources University of Washington Seattle WA

来源：评论

学校读者我要写书评

暂无评论

Towards creative design using collaborative interactive genetic algorithms

Towards creative design using collaborative interactive gene...

引用

Congress on evolutionary Computation

作者： Juan C. Quiroz Sushil J. Louis Amit Banerjee Sergiu M. Dascalu Evolutionary Computing Systems Laboratory Department of Computer Science & Engineering University of Nevada Reno NV USA School of Science Engineering and Technology Pennsylvania State University Harrisburg PA USA

We present a computational model of creative design based on collaborative interactive genetic algorithms. We test our model on floorplanning. We guide the evolution of floorplans based on subjective and objective criteria. The subjective criteria consists of designers picking the floorplan they like the best from a population of floorplans, and the objective criteria consists of coded architectural guidelines. We support collaboration by allowing individual designers to view each others' designs during the evolutionary process and the sharing of designs via case injection. This methodology supports team design, and reflects the view of creativity that collaboration accounts for much of our intelligence and creativity. We present a description of the model and a comparative study of floorplans created individually versus collaboratively. Our results show that floorplans created collaboratively were considered to be more ldquorevolutionaryrdquo and ldquooriginalrdquo than those created individually.

关键词： Algorithm design and analysis Collaboration Genetic algorithms Computational modeling Collaborative work Space exploration Design methodology Process design computer science Testing

来源：评论

学校读者我要写书评

暂无评论

Meeting Report: BioSharing at ISMB 2010

引用

Standards in genomic sciences 2010年第3期3卷 254-8页

作者： Dawn Field Susanna Sansone Edward F Delong Peter Sterk Iddo Friedberg Pascale Gaudet Susanna Lewis Renzo Kottmann Lynette Hirschman George Garrity Guy Cochrane John Wooley Folker Meyer Sarah Hunter Owen White Brian Bramlett Susan Gregurick Hilmar Lapp Sandra Orchard Philippe Rocca-Serra Alan Ruttenberg Nigam Shah Chris Taylor Anne Thessen Centre for Ecology & Hydrology Wallingford UK Oxford e-Research Centre University of Oxford Oxford UK Department of Biological Engineering Massachusetts Institue of Technology Cambridge USA Department of Microbiology Miami University Oxford USA Department of Computer Science and Software Engineering Miami University Oxford USA Swiss Institute of Bioinformatics Geneva 4 Switzerland Lawrence Berkeley National Laboratory Berkeley USA Microbial Genomics Group Max Planck Institute for Marine Microbiology & Jacobs University Bremen Bremen Germany Information Technology Center The MITRE Corporation Bedford USA Department of Microbiology and Molecular Genetics Michigan State University East Lansing USA European Molecular Biology Laboratory (EMBL) Outstation European Bioinformatics Institute (EBI) Cambridge UK University of California San Diego La Jolla USA Argonne National Laboratory Argonne USA Institute for Genome Sciences University of Maryland School of Medicine Baltimore USA Lux Bio Group Portland USA Biological Systems Science Division Department of Energy Washington DC USA National Evolutionary Synthesis Center (NESCent) Durham USA Science Commons c/o Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory Cambridge USA Stanford Center for Biomedical Informatics Research Stanford USA Marine Biological Laboratory USA

This report summarizes the proceedings of the one day BioSharing meeting held at the Intelligent systems for Molecular Biology (ISMB) 2010 conference in Boston, MA, USA This inaugural BioSharing event was hosted by the Genomic Standards Consortium as part of its M3 & BioSharing special interest group (SIG) workshop. The BioSharing event included invited talks from a range of community leaders and a panel discussion at the end of the day. The panel session led to the formal agreement among community leaders to join together to promote cross-community knowledge exchange and collaborations. A key focus of the newly formed Biosharing community will be linking up resources to promote real-world data sharing (virtuous cycle of data) and supporting compliance with data policies through the creation of a one-stop-portal of information. Further information about the newly established BioSharing effort can be found at http://***.

关键词： Community Leader Funding Agency Special Interest Group Panel Discussion Standardization Effort

来源：评论

学校读者我要写书评

暂无评论

Neuro-evolving maintain-station behavior for realistically simulated boats

Neuro-evolving maintain-station behavior for realistically s...

引用

Congress on evolutionary Computation

作者： Nathan A. Penrod David Carr Sushil J. Louis Bobby D. Bryant Evolutionary Computing System Laboratory in the Department of Computer Science and Engineering University of Nevada Reno NV USA

We evolve a neural network controller for a boat that learns to maintain a given bearing and range with respect to a moving target in the Lagoon 3D game environment. Simulating realistic physics makes maneuvering boats difficult and thus makes an evolutionary approach an attractive alternative to hand coded methods. We evolve the weights of simple recurrent neural networks trained with a fitness function designed to combine multiple fitness objectives based on speed, heading, and position to create a robust maintain station behavior. Results with an enforced subpopulation neural-evolution genetic algorithm indicate that we can consistently evolve robust maintain controllers for realistically simulated boats in Lagoon.

关键词： Boats Lead Artificial neural networks Neurons Robustness Robot sensing systems Distance measurement

来源：评论

学校读者我要写书评

暂无评论

A recursive clustering methodology using a genetic algorithm

A recursive clustering methodology using a genetic algorithm

引用

Congress on evolutionary Computation

作者： Amit Banerjee Sushil J. Louis Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering University of Nevada슠Reno NV USA

This paper presents a recursive clustering scheme that uses a genetic algorithm-based search in a dichotomous partition space. The proposed algorithm makes no assumption on the number of clusters present in the dataset; instead it recursively uncovers subsets in the data until all isolated and separated regions have been classified as clusters. A test of spatial randomness serves as a termination criteria for the recursive process. Within each recursive step, a genetic algorithm searches the partition space for an optimal dichotomy of the dataset. A simple binary representation is used for the genetic algorithm, along with classical selection, crossover and mutation operators. Results of clustering on test cases, ranging from simple datasets in 2-D to large multidimensional datasets compare favorably with state of the art approaches in genetic algorithm-driven clustering.

关键词： Genetic algorithms evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

A Genetic Algorithm Implementation of the Fuzzy Least Trimmed Squares Clustering

A Genetic Algorithm Implementation of the Fuzzy Least Trimme...

引用

IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Amit Banerjee Sushil J. Louis Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering MS/171 University of Nevada Reno NV USA

This paper describes a new approach to finding a global solution for the fuzzy least trimmed squares clustering. The least trimmed squares (LTS) estimator is known to be a high breakdown estimator, in both regression and clustering. From the point of view of implementation, the feasible solution algorithm is one of the few known techniques that guarantees a global solution for the LTS estimator. The feasible solution algorithm divides a noisy data set into two parts -the non-noisy retained set and the noisy trimmed set, by implementing a pairwise swap of datum between the two sets until a least squares estimator provides the best fit on the retained set. We present a novel genetic algorithm-based implementation of the feasible solution algorithm for fuzzy least trimmed squares clustering, and also substantiate the efficacy of our method by three examples.

关键词： Genetic algorithms Clustering algorithms Electric breakdown Least squares approximation Partitioning algorithms Noise robustness Laboratories computer science Minimization methods Prototypes

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：