1,319 research outputs found

    Application of decision trees and multivariate regression trees in design and optimization

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    Induction of decision trees and regression trees is a powerful technique not only for performing ordinary classification and regression analysis but also for discovering the often complex knowledge which describes the input-output behavior of a learning system in qualitative forms;In the area of classification (discrimination analysis), a new technique called IDea is presented for performing incremental learning with decision trees. It is demonstrated that IDea\u27s incremental learning can greatly reduce the spatial complexity of a given set of training examples. Furthermore, it is shown that this reduction in complexity can also be used as an effective tool for improving the learning efficiency of other types of inductive learners such as standard backpropagation neural networks;In the area of regression analysis, a new methodology for performing multiobjective optimization has been developed. Specifically, we demonstrate that muitiple-objective optimization through induction of multivariate regression trees is a powerful alternative to the conventional vector optimization techniques. Furthermore, in an attempt to investigate the effect of various types of splitting rules on the overall performance of the optimizing system, we present a tree partitioning algorithm which utilizes a number of techniques derived from diverse fields of statistics and fuzzy logic. These include: two multivariate statistical approaches based on dispersion matrices, an information-theoretic measure of covariance complexity which is typically used for obtaining multivariate linear models, two newly-formulated fuzzy splitting rules based on Pearson\u27s parametric and Kendall\u27s nonparametric measures of association, Bellman and Zadeh\u27s fuzzy decision-maximizing approach within an inductive framework, and finally, the multidimensional extension of a widely-used fuzzy entropy measure. The advantages of this new approach to optimization are highlighted by presenting three examples which respectively deal with design of a three-bar truss, a beam, and an electric discharge machining (EDM) process

    Multiobjective optimization in bioinformatics and computational biology

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    Model fusion using fuzzy aggregation: Special applications to metal properties

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    To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments

    Inferring efficient operating rules in multireservoir water resource systems: A review

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    [EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. 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Journal of Water Resources Planning and Management, 144(7), 05018008. doi:10.1061/(asce)wr.1943-5452.0000945Bolouri-Yazdeli, Y., Bozorg Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Evaluation of Real-Time Operation Rules in Reservoir Systems Operation. Water Resources Management, 28(3), 715-729. doi:10.1007/s11269-013-0510-1Borgomeo, E., Mortazavi-Naeini, M., Hall, J. W., O’Sullivan, M. J., & Watson, T. (2016). Trading-off tolerable risk with climate change adaptation costs in water supply systems. Water Resources Research, 52(2), 622-643. doi:10.1002/2015wr018164Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S.-M., & Loáiciga, H. A. (2017). WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems. Journal of Water Resources Planning and Management, 143(1), 04016070. doi:10.1061/(asce)wr.1943-5452.0000716Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., & Loáiciga, H. A. (2015). 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A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run. Water Resources Research, 49(6), 3476-3486. doi:10.1002/wrcr.20295Castelletti, A., Pianosi, F., & Soncini-Sessa, R. (2008). Water reservoir control under economic, social and environmental constraints. Automatica, 44(6), 1595-1607. doi:10.1016/j.automatica.2008.03.003Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks in water resource modelling and management. Environmental Modelling & Software, 22(8), 1073-1074. doi:10.1016/j.envsoft.2006.06.001Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian Networks and participatory modelling in water resource management. Environmental Modelling & Software, 22(8), 1075-1088. doi:10.1016/j.envsoft.2006.06.003Celeste, A. B., & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429-1443. doi:10.1016/j.advwatres.2009.06.008Celeste, A. B., Curi, W. F., & Curi, R. C. (2009). Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil. Pesquisa Operacional, 29(1), 223-234. doi:10.1590/s0101-74382009000100011Chandramouli, V., & Raman, H. (2001). Multireservoir Modeling with Dynamic Programming and Neural Networks. Journal of Water Resources Planning and Management, 127(2), 89-98. doi:10.1061/(asce)0733-9496(2001)127:2(89)Chang, L.-C., & Chang, F.-J. (2001). Intelligent control for modelling of real-time reservoir operation. Hydrological Processes, 15(9), 1621-1634. doi:10.1002/hyp.226Chazarra, M., García-González, J., Pérez-Díaz, J. I., & Arteseros, M. (2016). Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Research, 130, 67-77. doi:10.1016/j.epsr.2015.08.014Chen, D., Leon, A. S., Fuentes, C., Gibson, N. L., & Qin, H. (2018). Incorporating Filters in Random Search Algorithms for the Hourly Operation of a Multireservoir System. Journal of Water Resources Planning and Management, 144(2), 04017088. doi:10.1061/(asce)wr.1943-5452.0000876Coerver, H. M., Rutten, M. M., & van de Giesen, N. C. (2018). Deduction of reservoir operating rules for application in global hydrological models. Hydrology and Earth System Sciences, 22(1), 831-851. doi:10.5194/hess-22-831-2018Côté, P., & Leconte, R. (2016). Comparison of Stochastic Optimization Algorithms for Hydropower Reservoir Operation with Ensemble Streamflow Prediction. Journal of Water Resources Planning and Management, 142(2), 04015046. doi:10.1061/(asce)wr.1943-5452.0000575Cui, L., & Kuczera, G. (2005). Optimizing water supply headworks operating rules under stochastic inputs: Assessment of genetic algorithm performance. Water Resources Research, 41(5). doi:10.1029/2004wr003517Culley, S., Noble, S., Yates, A., Timbs, M., Westra, S., Maier, H. R., … Castelletti, A. (2016). A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resources Research, 52(9), 6751-6768. doi:10.1002/2015wr018253Cunha, M. C., & Antunes, A. (2012). Simulated annealing algorithms for water systems optimization. WIT Transactions on State of the Art in Science and Engineering, 57-73. doi:10.2495/978-1-84564-664-6/04Dariane, A. B., & Momtahen, S. (2009). Optimization of Multireservoir Systems Operation Using Modified Direct Search Genetic Algorithm. Journal of Water Resources Planning and Management, 135(3), 141-148. doi:10.1061/(asce)0733-9496(2009)135:3(141)Das, B., Singh, A., Panda, S. N., & Yasuda, H. (2015). Optimal land and water resources allocation policies for sustainable irrigated agriculture. 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Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: A Blueprint to Safeguard Europe's Water Resources COM(2012) 673 final. Brussels Belgium.European Commission. (2012b). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: Report on the Review of the European Water Scarcity and Droughts Policy COM(2012) 672 final. Brussels Belgium.Fallah-Mehdipour, E., Bozorg Haddad, O., & Mariño, M. A. (2012). Real-Time Operation of Reservoir System by Genetic Programming. Water Resources Management, 26(14), 4091-4103. doi:10.1007/s11269-012-0132-zFazlali, A., & Shourian, M. (2017). A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach. 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    Clustering Solutions of Multiobjective Function Inlining Problem

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    Hard real time-systems are often small devices operating on batteries that must react within a given deadline, so they must satisfy their timing, code size, and energy consumption requirements. Since these three objectives contradict each other, compilers for real-time systems go towards multiobjective optimizations which result in sets of trade-off solutions. A system designer can use the solution sets to choose the most suitable system configuration. Evolutionary algorithms can find trade-off solutions but the solution set might be large which complicates the task of the system designer. We propose to divide the solution set into clusters, so the system designer chooses the most suitable cluster and examines a smaller subset in detail. In contrast to other clustering techniques, our method guarantees that the sizes of all clusters are less than a predefined limit. Our method clusters a set by using any existing clustering method, divides clusters with sizes exceeding the predefined size into smaller clusters, and reduces the number of clusters by merging small clusters. The method guarantees that the final clusters satisfy the size constraint. We demonstrate our approach by considering a well-known compiler-based optimization called function inlining. It substitutes function calls by the function bodies which decreases the execution time and energy consumption of a program but increases its code size

    Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms

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    The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator -based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization.info:eu-repo/semantics/submittedVersio
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