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A Study on the Use of multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2012年第2期5卷 231-253页

作者： Trawinski, Krzysztof Cordon, Oscar Quirin, Arnaud European Ctr Soft Comp Mieres Mieres 33600 Spain

In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strategy. A state-of-the-art evolutionary multiobjective algorithm, namely NSGA-II, is used to provide a component classifier selection and improve FURIA-based fuzzy MCS. We propose five different fitness functions based on three different optimization criteria, accuracy, complexity, and diversity. Twenty UCI high dimensional datasets were considered in order to conduct the experiments. A combination between accuracy and diversity criteria provided very promising results, becoming competitive with classical MCS learning methods.

关键词： Fuzzy rule-based multiclassification systems bagging FURIA genetic selection of individual classifiers diversity measures evolutionary multiobjective optimization NSGA-II

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Characterizing the performance of an image-based recognizer for planar mechanical linkages in textbook graphics and hand-drawn sketches

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COMPUTERS & GRAPHICS-UK 2015年第Nov.期52卷 1-17页

作者： Eicholtz, Matthew Kara, Levent Burak Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

In this work, we present a computational framework for automatically generating kinematic models of planar mechanical linkages from raw images. The hallmark of our approach is a novel combination of supervised learning methods for detecting mechanical parts (e.g. joints, rigid bodies) with the optimizing power of a multiobjective evolutionary algorithm, which concurrently maximizes image consistency and mechanical feasibility. A rigorous set of experiments was conducted to systematically evaluate the performance of each phase in our framework, comparing various combinations of joint and body detection schemes and feasibility constraints. Precision-recall curves are used to assess object detection performance. For the optimization, in addition to standard accuracy measures such as top-N accuracy, we introduce a new performance metric called user effort ratio that quantifies the amount of user interaction required to correct an inaccurate optimization solution. Current state-of-the-art performance is achieved with (i) one (or a cascade of) support vector machines for joint detection, (ii) foreground extraction to reduce false positives, (iii) supervised body detection using normalized geodesic time, distance, and detected joint confidence, and (iv) feasibility constraints derived from graph theory. The proposed framework generalizes moderately well from textbook graphics to hand-drawn sketches, and user effort ratio results demonstrate the potential power of an interactive system in which simple user interactions complement computer recognition for fast kinematic modeling. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Computer vision evolutionary multiobjective optimization Image processing Kinematic modeling Object recognition Sketch recognition

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Nature-inspired multiobjective patient stratification from cancer gene expression data

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INFORMATION SCIENCES 2020年 526卷 245-262页

作者： Wang, Yunhe Ma, Zhiqiang Wong, Ka-Chun Li, Xiangtao Jilin Univ Sch Artificial Intelligence Changchun 130012 Jilin Peoples R China Northeast Normal Univ Sch Informat Sci & Technol Changchun 130117 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Stratifying personalized treatment for patients has been one of the main challenges for modern medicine. To solve this problem, various clustering algorithms have been proposed for patient stratification in both quantification and biological ways meaningfully. However, most of the existing clustering algorithms still suffer from many realistic algorithm limitations such as low diagnostic ability and bad generalization. Therefore, to address those restrictions, we propose a novel multiobjective spectral clustering algorithm based on decomposition. A population that consists of distance weight and two other indispensable parameters of the spectral clustering is optimized by the proposed algorithm. Two cluster validity indices are proposed to capture the characteristics of different datasets. To validate the effectiveness and efficiency of the proposed algorithm, we benchmark it on thirty-five real patient stratification datasets and six real-world medical datasets across thousands of comparisons with fifteen algorithms, including ten effective clustering methods and five state-of-the-art multiobjective algorithms. The experimental results indicate that the proposed algorithm performs better than other compared algorithms with high clustering ability for patient stratification. Moreover, extensive analysis of time complexity and parameters are performed to prove the robustness of the proposed algorithm from different perspectives. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Clustering Spectral clustering evolutionary multiobjective optimization Patient stratification

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Handling constraints using multiobjective optimization concepts

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2004年第15期59卷 1989-2017页

作者： Aguirre, AH Riondal, SB Coello, CAC Lizárraga, GL Montes, EM CIMAT Ctr Res Math Dept Comp Sci Guanajuato 36240 Gto Mexico CINVESTAV IPN Evolutionary Computat Grp Dept Ingn ElectSecc Computac Mexico City 07300 DF Mexico

In this paper, we propose a new constraint-handling technique for evolutionary algorithms which we call inverted-shrinkable PAES (IS-PAES). This approach combines the use of multiobjective optimization concepts with a mechanism that focuses the search effort onto specific areas of the feasible region by shrinking the constrained search space. IS-PAES also uses an adaptive grid to store the solutions found, but has a more efficient memory-management scheme than its ancestor (the Pareto archived evolution strategy for multiobjective optimization). The proposed approach is validated using several examples taken from the standard evolutionary and engineering optimization literature. Comparisons are provided with respect to the stochastic ranking method (one of the most competitive constraint-handling approaches used with evolutionary algorithms currently available) and with respect to other four multiobjective-based constraint-handling techniques. Copyright (C) 2004 John Wiley Sons, Ltd.

关键词： evolutionary algorithms constraint-handling evolutionary multiobjective optimization evolutionary optimization

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Multicriteria optimization of the investment in the auxiliary services of thermal power plants: A case study

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ENERGY CONVERSION AND MANAGEMENT 2015年第Mar.期93卷 339-348页

作者： Ruiz, Ana B. Cabello, Jose M. Platero, Carlos A. Blazquez, Francisco Univ Malaga Dept Appl Econ Math E-29071 Malaga Spain Univ Politecn Madrid Dept Elect Engn E-28040 Madrid Spain

Thermal power plants have traditionally operated at rated power as base load, but nowadays they operate at partial loads because of the new situation of the electricity market. These plants have raised their auxiliary services consumption because, in most of the cases, the auxiliaries were not designed to efficiently operate at partial loads. This paper presents a multiple criteria study about the efficiency improvement of the auxiliary services. We consider the economic investment and the net present value, as economic criteria, together with the energy saving criterion. In the multiobjective model proposed, the energy model is validated using several measures taken in a 1100 megawatts coal power plant. Besides, the multiobjective problem associated to the case study considered is solved using evolutionary multiobjective optimization and considering preference information. The results obtained conclude that a significant efficiency improvement of the auxiliary services can be achieved by means of the improvement strategies considered. Indeed, the high net present values reached indicate that the investments required by the different solutions are really profitable from the economic perspective. Therefore, investing money in the efficiency improvement of the auxiliary services represents a very profitable option for improving the operation of power plants at partial loads. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Thermal power generation Power generation auxiliary systems Power generation economics evolutionary multiobjective optimization Decision-making

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On upper approximations of Pareto fronts

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JOURNAL OF GLOBAL optimization 2018年第3期72卷 475-490页

作者： Kaliszewski, I. Miroforidis, J. Polish Acad Sci Syst Res Inst Ul Newelska 6 PL-01447 Warsaw Poland Warsaw Sch Informat Technol Ul Newelska 6 PL-01447 Warsaw Poland

In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists. As it is not always the case, in this paper we give some sufficient conditions for the existence of upper shells. We also investigate how to constructively search infeasible sets to derive upper shells. We approach this issue by means of problem relaxations. We formally show that under certain conditions some subsets of lower shells to relaxed multiobjective optimization problems are upper shells in the respective unrelaxed problems. Results are illustrated by a numerical example representing asmall but real mechanical problem. Practical implications of the results are discussed.

关键词： multiobjective optimization Global Pareto optimum Two-sided Pareto front approximations Pareto front upper approximation existence evolutionary multiobjective optimization

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Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第3期18卷 348-365页

作者： Li, Miqing Yang, Shengxiang Liu, Xiaohui Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England De Montfort Univ Ctr Computat Intelligence CCI Sch Comp Sci & Informat Leicester LE1 9BH Leics England

It is commonly accepted that Pareto-based evolutionary multiobjective optimization (EMO) algorithms encounter difficulties in dealing with many-objective problems. In these algorithms, the ineffectiveness of the Pareto dominance relation for a high-dimensional space leads diversity maintenance mechanisms to play the leading role during the evolutionary process, while the preference of diversity maintenance mechanisms for individuals in sparse regions results in the final solutions distributed widely over the objective space but distant from the desired Pareto front. Intuitively, there are two ways to address this problem: 1) modifying the Pareto dominance relation and 2) modifying the diversity maintenance mechanism in the algorithm. In this paper, we focus on the latter and propose a shift-based density estimation (SDE) strategy. The aim of our study is to develop a general modification of density estimation in order to make Pareto-based algorithms suitable for many-objective optimization. In contrast to traditional density estimation that only involves the distribution of individuals in the population, SDE covers both the distribution and convergence information of individuals. The application of SDE in three popular Pareto-based algorithms demonstrates its usefulness in handling many-objective problems. Moreover, an extensive comparison with five state-of-the-art EMO algorithms reveals its competitiveness in balancing convergence and diversity of solutions. These findings not only show that SDE is a good alternative to tackle many-objective problems, but also present a general extension of Pareto-based algorithms in many-objective optimization.

关键词： Convergence diversity evolutionary multiobjective optimization many-objective optimization shift-based density estimation

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Constrained Subproblems in a Decomposition-Based multiobjective evolutionary Algorithm

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2016年第3期20卷 475-480页

作者： Wang, Luping Zhang, Qingfu Zhou, Aimin Gong, Maoguo Jiao, Licheng Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England E China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China

A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.

关键词： Constraint decomposition approach evolutionary multiobjective optimization

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A methodology for the multi-objective shape optimization of thin noise barriers

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APPLIED MATHEMATICAL MODELLING 2017年 50卷 656-675页

作者： Toledo, R. Aznarez, J. J. Greiner, D. Maeso, O. Univ Las Palmas Gran Canaria Inst Univ Sistemas Inteligentes & Aplicac Numer I Las Palmas Gran Canaria Las Palmas Spain

The approach of this paper is based on the evolutionary multi-objective optimization (EMO) of very thin noise barrier models with improved performance idealized as single wire designs. To assume such a simplification of reality, the dual boundary element (DBE) formulation for assessing the acoustic efficiency arises as the most appropriate strategy involving BE to avoid drawbacks associated with the exclusive implementation of the standard formulation (SBE). The 2D analysis performed in this work focuses on the simultaneous optimization of two objectives in conflict using the Non-dominated Sorting Genetic Algorithm (NSGA-II): the maximization of noise attenuation and the minimization of the amount of material used in manufacturing the barrier, represented by the overall length of its elements (this function is, closely related to the final cost of the device). Under this framework, two optimization strategies are compared for each model with equal number of fitness evaluations: (1) when considering a random initial population and (2) when including the best single-objective optimal design in the initial population. The results obtained show wide and uniformly spread-out non-dominated fronts, reflected in the geometric diversity featured by optimal designs;statistical analysis confirm the advantages of the latter initial population strategy. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Thin noise barriers Acoustic efficiency Shape optimization evolutionary multiobjective optimization Dual boundary element formulation

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A multiobjective Exploratory Procedure for Regression Model Selection

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2015年第1期24卷 154-182页

作者： Sinha, Ankur Malo, Pekka Kuosmanen, Timo Indian Inst Management Ahmadabad 380015 Gujarat India Aalto Univ Sch Business Dept Informat & Serv Econ FIN-00101 Helsinki Finland

Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by over-abundance of explanatory variables, nonlinearities, and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance of the variables. To provide a robust method for model selection, this article introduces the multiobjective genetic algorithm for variable selection (MOGA-VS) that provides the user with an optimal set of regression models for a given dataset. The algorithm considers the regression problem as a two objective task, and explores the Pareto-optimal (best subset) models by preferring those models over the other which have less number of regression coefficients and better goodness of fit. The model exploration can be performed based on in-sample or generalization error minimization. The model selection is proposed to be performed in two steps. First, we generate the frontier of Pareto-optimal regression models by eliminating the dominated models without any user intervention. Second, a decision-making process is executed which allows the user to choose the most preferred model using visualizations and simple metrics. The method has been evaluated on a recently published real dataset on Communities and Crime Within the United States.

关键词： evolutionary multiobjective optimization Automated regression Genetic algorithm Multiple criteria decision making

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