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multi-objective clustering ensemble for gene expression data analysis

NEUROCOMPUTING 2009年第13-15期72卷 2763-2774页

作者： Faceli, Katti de Souto, Marcilio C. R. de Araujo, Daniel S. A. de Carvalho, Andre C. P. L. F. Univ Fed Sao Carlos BR-18052780 Sorocaba SP Brazil Univ Fed Rio Grande do Norte Dept Informat & Matemat Aplicada BR-59072970 Natal RN Brazil Univ Sao Paulo Inst Ciencias Matemat & Comp Dept Ciencias Comp & Estat BR-13560970 Sao Carlos SP Brazil

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Cluster analysis multi-objective genetic algorithms Gene expression data Model selection Ensemble

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A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making

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REQUIREMENTS ENGINEERING 2009年第4期14卷 231-245页

作者： Finkelstein, Anthony Harman, Mark Mansouri, S. Afshin Ren, Jian Zhang, Yuanyuan UCL London WC1E 6BT England Kings Coll London London WC2R 2LS England Brunel Univ Uxbridge UB8 3PH Middx England

This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space. The paper also reports on experiments to determine the most suitable algorithm for this problem, comparing the results of the NSGA-II algorithms, a widely used multi objective evolutionary algorithm, and the Two-Archive evolutionary algorithm, a recently proposed alternative.

关键词： Pareto optimality Fairness analysis Requirements assignment multi-objective genetic algorithms Decision making

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multiple-objective genetic optimization of the spatial design for packing and distribution carton boxes

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COMPUTERS & INDUSTRIAL ENGINEERING 2008年第4期54卷 889-902页

作者： Leung, S. Y. S. Wong, W. K. Mok, P. Y. Hong Kong Polytech Univ Inst Text & Clothing Kowloon Hong Kong Peoples R China

Packing and cutting problems, which dealt with filling up a space of known dimension with small pieces, have been an attractive research topic to both industry and academia. Comparatively, the number of reported studies is smaller for container spatial design, i.e., defining the optimal container dimension for packing small pieces of goods with known sizes so that the container space utilization is maximized. This paper aims at searching an optimal set of carton boxes for a towel manufacturer so as to lower the overall future distribution costs by improving the carton space utilization and reducing the number of carton types required. A multi-objective genetic algorithm (MOGA) is used to search the optimal design of carton boxes for a one-week sales forecast and a 53-week sales forecast. Clustering techniques are then used to study the order pattern of towel products in order to validate the genetically generated results. The results demonstrate that MOGA effectively search the best carton box spatial design to reduce unfilled space as well as the number of required carton types. It is important to note that the proposed methodology for optimal container design is not limited to the apparel industry but practically attractive and applicable to every industry which aims for distribution costs reduction. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithms clustering technique packing and cutting container design

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Neuro-genetic non-invasive temperature estimation: Intensity and spatial prediction

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2008年第2期43卷 127-139页

作者： Teixeira, Cesar A. Ruano, M. Graca Ruano, Antonio E. Pereira, Wagner C. A. Univ Algarve Ctr Intelligent Syst Fac Sci & Technol P-8005139 Faro Portugal Univ Fed Rio de Janeiro Biomed Engn Program COPPE BR-21941972 Rio De Janeiro Brazil

objectives: The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In this work, radial basis functions artificial neural networks were constructed to access temperature evolution on an ultrasound insonated medium. Methods: The employed models were radial basis functions neural networks with external dynamics induced by their inputs. Both the most suited set of model inputs and number of neurons in the network were found using the multi-objective genetic algorithm. The neural models were validated in two situations: the operating ones, as used in the construction of the network;and in 11 unseen situations. The new data addressed two new spatial locations and a new intensity level, assessing the intensity and space prediction capacity of the proposed model. Results: Good performance was obtained during the validation process both in terms of the spatial points considered and whenever the new intensity level was within the range of applied intensities. A maximum absolute error of 0.5 degrees C +/- 10% (0.5 degrees C is the gold-standard threshold in hyperthermia/diathermia) was attained with low computationally complex models. Conclusion: The results confirm that the proposed neuro-genetic approach enables foreseeing temperature propagation, in connection to intensity and space parameters, thus enabling the assessment of different operating situations with proper temperature resolution. (C) 2008 Elsevier B.V. All rights reserved.

关键词： black-box modelling artificial neural networks multi-objective genetic algorithms non-invasive temperature estimation

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Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm

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PATTERN ANALYSIS AND APPLICATIONS 2008年第2期11卷 159-168页

作者： Thilagam, P. Santhi Ananthanarayana, V. S. Natl Inst Technol Karnataka Dept Comp Engn Srinivasnagar 575025 Surathkal India Natl Inst Technol Karnataka Dept Informat Technol Srinivasnagar 575025 Surathkal India

Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.

关键词： fuzzy association rules multi-objective genetic algorithms fuzzy k-means clustering

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genetic algorithms for signal grouping in sensor validation: A comparison of the filter and wrapper approaches

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Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2008年第2期222卷 189-206页

作者： Baraldi, P. Zio, E. Gola, G. Hoffmann, M. Roverso, D. Department of Nuclear Engineering Polytechnic of Milan Milano Italy OECD Halden Reactor Project Institutt for energiteknikk Halden Norway

Sensor validation is aimed at detecting anomalies in sensor operation and reconstructing the correct signals of failed sensors, e.g. by exploiting the information coming from other measured signals. In field applications, the number of signals to be monitored can often become too large to be handled by a single validation and reconstruction model. To overcome this problem, the signals can be subdivided into groups according to specific requirements and a number of validation and reconstruction models can be developed to handle the individual groups. In this paper, multi-objective genetic algorithms (MOGAs) are devised for finding groups of signals bearing the required characteristics for constructing signal validation and reconstruction models based on principal component analysis (PCA). Two approaches are considered for the MOGA search of the signal groups: the filter and wrapper approaches. The former assesses the merits of the groups only from the characteristics of their signals, whereas the latter looks for those groups optimal for building the models actually used to validate and reconstruct the signals. The two approaches are compared with respect to a real case study concerning the validation of 84 signals collected from a Swedish boiling water nuclear power plant. © 2008, Institution of Mechanical Engineers. All rights reserved.

关键词： fitter/wrapper multi-objective genetic algorithms principal component analysis sensor monitoring signal validation

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Robust design and optimization for autonomous PV-wind hybrid power systems

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第3期9卷 401-409页

作者： Jun-hai SHI Zhi-dan ZHONG Xin-jian ZHU Guang-yi CAO Institute of Fuel Cells Shanghai Jiao Tong University Shanghai 200240 China

This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.

关键词： PV-wind power system Robust design Constraint multi-objective optimizations multi-objective genetic algorithms Monte Carlo Simulation （MCS） Latin Hypercube Sampling （LHS）

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Events group risk importance by genetic algorithms

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Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2008年第3期222卷 337-346页

作者： Podofillini, L. Zio, E. Risk and Human Reliability Group Paul Scherrer Institut United States Department of Energy Polytechnic of Milan Milan Italy

In this paper, the power of genetic algorithms is exploited to search for the most important groups of events in a risk/reliability model. To this end a two-objective search is formulated in which the decision variables are the groups of events, and the objectives are to maximize the importance of the groups, while minimizing their dimension. This allows the identification of the most important single events, couples of events, triplets, and so forth. Three case studies concerning network systems of increasing complexity are analysed. © 2008, Institution of Mechanical Engineers. All rights reserved.

关键词： differential importance measure Fussell—Vesely importance measure group importance measures multi-objective genetic algorithms multi-objective optimization network systems redundancy allocation

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Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery

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APPLIED SOFT COMPUTING 2008年第4期8卷 1365-1380页

作者： Zio, E. Baraldi, P. Gola, G. Politecn Milan Dept Nucl Engn I-20133 Milan Italy

Recent researches in fault classification have shown the importance of accurately selecting the features that have to be used as inputs to the diagnostic model. In this work, a multi-objective genetic algorithm (MOGA) is considered for the feature selection phase. Then, two different techniques for using the selected features to develop the fault classification model are compared: a single classifier based on the feature subset with the best classification performance and an ensemble of classifiers working on different feature subsets. The motivation for developing ensembles of classifiers is that they can achieve higher accuracies than single classifiers. An important issue for an ensemble to be effective is the diversity in the predictions of the base classifiers which constitute it, i.e. their capability of erring on different sub-regions of the pattern space. In order to show the benefits of having diverse base classifiers in the ensemble, two different ensembles have been developed: in the first, the base classifiers are constructed on feature subsets found by MOGAs aimed at maximizing the fault classification performance and at minimizing the number of features of the subsets;in the second, diversity among classifiers is added to the MOGA search as the third objective function to maximize. In both cases, a voting technique is used to effectively combine the predictions of the base classifiers to construct the ensemble output. For verification, some numerical experiments are conducted on a case of multiple-fault classification in rotating machinery and the results achieved by the two ensembles are compared with those obtained by a single optimal classifier. (C) 2007 Elsevier B.V. All rights reserved.

关键词： fault diagnosis feature selection multi-objective genetic algorithms ensemble rotating machinery

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Research on integrated optimization design of hypersonic cruise vehicle

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AEROSPACE SCIENCE AND TECHNOLOGY 2008年第7期12卷 567-572页

作者： Jing, Che Shuo, Tang NW Polytech Univ Coll Astronaut Xian 710072 Shannxi Peoples R China

Optimization design is the most important key technique of Air-breathing Hypersonic Cruise Vehicle (HCV). To improve the design level and get better integrated performances of HCV, this paper researches the integrated optimization design method of waverider hypersonic cruise vehicle. In the optimization design, multi-objective genetic algorithms (MOGA) is selected as the optimization algorithm, and the shape parameters of aircraft are as design variables. Some performances, such as aerodynamics, aeroheating, radar cross section (RCS), airframe/scramjet integration, and volume of airframe, trimmed characteristic, static stability and maneuverability at cruise phase are selected as the objectives. When the optimization process finishes, the Pareto front side is got, in which many Pareto solutions whose integrated performances are more excellent than basic configuration are found. According to the design idea, we choose a Pareto solution from the Pareto front side as the recommended shape configuration for further research. To validate the aerodynamics of recommended configuration, wind tunnel test is done. And the comparison results show that the optimization design is successful. (C) 2008 Elsevier Masson SAS. All rights reserved.

关键词： multi-objective genetic algorithms Integrated optimization design Hypersonic cruise vehicle Airframe/scramjet integration Pareto front side

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