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2008 IEEE Swarm Intelligence Symposium, SIS 2008

作者： Santana-Quintero, Luis V. Coello Coello, Carlos A. Hernández-Díaz, Alfredo G. Velázquez, Jesús Moisés Osorio CINVESTAV-IPN Computer Science Department Mexico City Mexico Pablo de Olavide University Department of Quantitative Methods Seville Spain Instituto Tecnológico de Culiacán Systems and Informatics Department Culiacán Sinaloa Mexico

ISBN: (纸本)9781424427055

This paper presents a new algorithm that approximates real function evaluations using supervised learning with a surrogate method called support vector machine (SVM). We perform a comparative study among different leader selection schemes in a Multi-Objective Particle Swarm Optimizer (MOPSO), in order to determine the most appropriate approach to be adopted for solving the sort of problems of our interest. The resulting hybrid presents a poor spread of solutions, which motivates the introduction of a second phase to our algorithm, in which an approach called rough sets is adopted in order to improve the spread of solutions along the Pareto front. Rough sets are used as a local search engine, which is able to generate solutions in the neighborhood of the nondominated solutions previously generated by the surrogate-based algorithm. The resulting approach is able to generate reasonably good approximations of the Pareto front of problems of up to 30 decision variables with only 2,000 fitness function evaluations. Our results are compared with respect to the NSGA-II, which is a multi-objective evolutionary algorithm representative of the state-of-the-art in the area. ©2008 IEEE.

关键词： Function evaluation

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Test suite optimizing strategy in regression testing

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 2008年第SUPPL. 2期38卷 184-190页

作者： Sun, Ji-Rong Li, Zhi-Shu Ni, Jian-Cheng Li, Bao-Lin School of Computer Science Sichuan University Chengdu 610065 China Teaching Affairs Department Sichuan Radio and TV University Chengdu 610073 China

Three algorithms are proposed in optimizing the regression test suite when the test suite reduction technique and test case prioritization technique are combined together. 1) Build-up algorithm, which first selects the essential test case and then the one with the biggest additional contribution until all the requirements are satisfied. 2) Break-down algorithm, which iteratively discards the redundant test case with the smallest contribution until all the test cases are essential or all the requirements are satisfied. 3) Test case prioritization strategy iteratively uses build-up algorithm until all the test cases are ordered. Each test-case contribution is not only related to the importance of each requirement but also to the whole requirements set. Experimental studies are performed to show that test-suite reduction combined with the test-suite prioritization technique can provide a smaller-sized test suite with a higher ARRS value.

关键词： Iterative methods

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Memetic Pareto Evolutionary Artificial Neural Networks for the Determination of Growth Limits of Listeria Monocytogenes

Memetic Pareto Evolutionary Artificial Neural Networks for t...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： J.C. Fernández P.A. Gutiérrez C. Hervás F.J. Martínez Department of Computer Science University of Córdoba Cordoba Spain Department of Management and Quantitative Methods ETEA Cordoba Spain

The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a memetic Pareto evolutionary NSGA2 (MPENSGA2) approach based on the Pareto-NSGAII evolution (PNSGAII) algorithm. We propose to augmente it with a local search using the improved Rprop-IRprop algorithm for the prediction of growth/no growth of L. monocytogenes as a function of the storage temperature, pH, citric (CA) and ascorbic acid (AA). The results obtained show that the generalization ability can be more efficiently improved within a framework that is multi-objective instead of a within a single-objective one.

关键词： Sensitivity Classification algorithms Testing Accuracy Artificial neural networks Training Neurons

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Erratum to:Using Avida-ED for teaching and Learning About Evolution in Undergraduate Introductory Biology Courses

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Evolution: Education and Outreach 2009年第4期2卷 742-742页

作者： Elena Bray Speth Tammy M. Long Robert T. Pennock Diane Ebert-May Center for Research in College Science Teaching and Learning Michigan State University 270 Plant Biology East Lansing MI 48824 USA Biological Sciences Program Michigan State University East Lansing MI 48824 USA Lyman Briggs College and Departments of Philosophy and Computer Science Michigan State University East Lansing MI 48824 USA Department of Plant Biology Michigan State University East Lansing MI 48824 USA

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Evolutionary learning by a sensitivity-accuracy approach for multi-class problems

Evolutionary learning by a sensitivity-accuracy approach for...

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Congress on Evolutionary Computation

作者： F.J. Martinez-Estudillo P.A. Gutierrez C. Hervas J.C. Fernandez Department of Management and Quantitative Methods ETA Cordoba Spain Department of Computer Science and Numerical Analysis University of Córdoba Cordoba Spain

Performance evaluation is decisive when improving classifiers. Accuracy alone is insufficient because it cannot capture the myriad of contributing factors differentiating the performances of two different classifiers and approaches based on a multi-objective perspective are hindered by the growing of the Pareto optimal front as the number of classes increases. This paper proposes a new approach to deal with multi-class problems based on the accuracy (C) and minimum sensitivity (S) given by the lowest percentage of examples correctly predicted to belong to each class. From this perspective, we compare different fitness functions (accuracy, C , entropy, E , sensitivity, S , and area, A ) in an evolutionary scheme. We also present a two stage evolutionary algorithm with two sequential fitness functions, the entropy for the first step and the area for the second step. This methodology is applied to solve six benchmark classification problems. The two-stage approach obtains promising results and achieves a high classification rate level in the global dataset with an acceptable level of accuracy for each class.

关键词： Classification algorithms Accuracy Sensitivity Artificial neural networks Evolutionary computation Entropy Multilayer perceptrons

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Guidelines for podcast production and use in tertiary education

Guidelines for podcast production and use in tertiary educat...

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25th Annual Conference of the Australasian Society for computers in Learning in Tertiary Education - "Hello? Where are you in the landscape of educational technology?", ASCILITE 2008

作者： O'Donoghue, Michael Hoskin, Alan Bell, Tim University Centre for Teaching and Learning University of Canterbury Private Bag 4800 Christchurch 8140 New Zealand Department of Computer Science and Software Engineering College of Engineering University of Canterbury Private Bag 4800 Christchurch 8140 New Zealand

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Parallel FDTD Simulation of Two-Dimensional Frequency Selective Surface

Parallel FDTD Simulation of Two-Dimensional Frequency Select...

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ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM

作者： Min Li Jian Zhou Anchun Xiao Yi Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Science and Technology Wuhan Hubei China School of Science Hubei University of Technology Wuhan China Computer Teaching Department of Basic Courses Institute Zhejiang Wanli University Ningbo China

Grid computing is an active field in high-availability computing. This paper focuses on the numerical calculation of two-dimensional frequency selective surface using the finite difference time domain (FDTD) method on grid-enabled cluster. The result of experiment shows that the evaluation via the parallel FDTD is in good agreement with the result obtained via the traditional FDTD method. The parallel FDTD algorithm is correct and reasonable, which can be extended to other periodic structure and finite exciting sources.

关键词： Finite difference methods Time domain analysis Computational modeling Grid computing Electromagnetics Artificial neural networks Periodic structures

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A Comparative Evaluation of Mode Switching Techniques

A Comparative Evaluation of Mode Switching Techniques

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Chuanyi Liu Xiangshi Ren Dawei Li Kochi University of Technology School of Mathematics & Computer Science Yunnan University of Nationalities Japan Department of Information Systems Engineering Kochi University of Technology Kochi Japan Teaching and Research Center of Public Computer Jilin University Changchun China

In this study six different mode switching techniques (i.e. timeout mode switching, non-preferred hand mode switching, barrel button mode switching, pressure mode switching, tilt mode switching and azimuth mode switching) based on multiple parameters pen input are proposed. The results indicate that the techniques utilizing tilt angle and azimuth offer faster performance than the others.

关键词： Switches Azimuth Ink Phase detection Distributed processing Application software Mathematics computer science Information systems Systems engineering and theory

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Work in progress - computing and undergraduate engineering: A collaborative process to align computing education with engineering workforce needs (CPACE)

Work in progress - computing and undergraduate engineering: ...

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Frontiers in Education (FIE) Conference

作者： Claudia E. Vergara Mark Urban-Lurain Daina Briedis Neeraj Buch Jeannine LaPrad Louise Paquette Jon Sticklen Thomas F. Wolff Center for Research on College Science Teaching and Learning. MSU USA Instructional Technology Research & Development Division of Science and Mathematics Education College of Natural Science. MSU USA Chemical Engineering and Materials Science. MSU USA Civil and Environmental Engineering. MSU USA CSW USA Math and Computer Science Department. LCC USA Computer Science and Engineering. MSU USA College of Engineering MSU USA

This NSF-funded community-building (CB) project brings together Michigan State University (MSU), Lansing Community College (LCC), and the Corporation for a Skilled Workforce (CSW) to design and implement a process to create a collaboratively defined undergraduate computing education within the engineering and technology fields in alignment with the computational problem-solving abilities needed to transform mid-Michiganpsilas economy and workforce. In this WIP we outline the process we are developing to ensure that a wide variety of stakeholders - business, community leaders and post secondary educators - collaborate to identify workforce computational skills, define how these skills can be integrated across a curriculum, and develop revised curricula that integrate computational problem-solving across engineering departmental courses. By documenting, evaluating and making the process explicit, this process can serve as a model for national efforts to revitalize undergraduate computing education in engineering, and should be extensible to other computing education reform efforts.

关键词： Collaborative work Problem-solving computer science education Business Design engineering Educational institutions Educational programs Educational technology computer science Mirrors

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Research on Author's Semantic Similarity Based on Collaborative Network

Research on Author's Semantic Similarity Based on Collaborat...

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International Conference on Information Technology: New Generations (ITNG)

作者： Kai-zhong Jiang Yuan-Qiong Wu Zhao Lv Jun-Zhong Gu East China Normal University Shanghai CN (College of Basic Teaching Shanghai University of Engineering Science Shanghai 201620China) Department of Computer Science and Technology East China Normal University Shanghai200062 China)

The community is not only one kind of widely existing organization in networks, but also contains distinct information about topics. The present paper is to define a metric between community members, weigh their semantic similarity, and finally make use of the metric to find more about the Web. In order to achieve this, the usual practice is to establish a "plane" adjacency matrix according to the citation relationship among all community members. However, it is easy to trigger the problem of topic drift. To overcome this weak point, the present paper puts forward firstly the strategy of establishing a three-order adjacency tensor on the 3-dimensional relationship between the seed document, simple document and the author. Secondly, the adjacency tensor is decomposed to obtain the principal component in each dimension. Thirdly, the semantic similarity between authors is defined. The experiment makes it clear that the semantic similarity between the author and people of importance tends to be stable under a particular circumstance.

关键词： Collaborative work computer science Tensile stress Space technology Information technology Education Electronic mail Text analysis Social network services Citation analysis

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