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10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Qiu, Xin Huang, Ye Xu, Jian-Xin Tan, Kay Chen Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore 117456 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

ISBN: (纸本)9783319135632;9783319135625

multi-objective evolutionary algorithms (MOEAs) are popular approaches for solving multi-objective problems (MOPs). One representative method is Non-dominated Sorting Genetic algorithm II (NSGA-II), which has achieved great success in the field by introducing non-dominated sorting into survival selection. However, as a common issue for dominance-based algorithms, the performance of NSGA-II will decline in solving problems with 3 or more objectives. This paper aims to circumvent this issue by incorporating the concept of decomposition into NSGA-II. A grouping-based hybrid multi-objective optimization framework is proposed for tackling 3-objective problems. Original MOP is decomposed into several scalar subproblems, and each group of population is assigned with two scalar subproblems as new objectives. In order to better cover the whole objective space, new objective spaces are formulated via rotating the original objective space. Simulation results show that the performance of the proposed algorithm is competitive when dealing with 3-objective problems.

关键词： multi-objective evolutionary algorithm hybrid decomposition

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multi-objective evolutionary algorithm Based on Correlativity and Its Application

Multi-objective Evolutionary Algorithm Based on Correlativit...

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7th World Congress on Intelligent Control and Automation

作者： Li, Junfeng Dai, Wenzhan Zhejiang Sci Tech Univ Dept Automat Control Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781424421138

In this paper, based on correlativity theory, a kind of multi-objective evolutionary algorithm is put forward. First, the best solution of every objective among the mufti-objectives is obtained and they are regarded on as the referenced vector. Second, the correlativity index between every individual and the referenced vector is solved and the correlativity index is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.

关键词： multi-objective evolutionary algorithm Degree of grey incidence Included angle cosine

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Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第1期58卷 37-49页

作者： Cruz-Ramirez, Manuel Hervas-Martinez, Cesar Carlos Fernandez, Juan Briceno, Javier de la Mata, Manuel Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Univ Hosp Reina Sofia Liver Transplantat Unit Cordoba 14004 Spain

objective: The optimal allocation of organs in liver transplantation is a problem that can be resolved using machine-learning techniques. Classical methods of allocation included the assignment of an organ to the first patient on the waiting list without taking into account the characteristics of the donor and/or recipient. In this study, characteristics of the donor, recipient and transplant organ were used to determine graft survival. We utilised a dataset of liver transplants collected by eleven Spanish hospitals that provides data on the survival of patients three months after their operations. Methods and material: To address the problem of organ allocation, the memetic Pareto evolutionary non-dominated sorting genetic algorithm 2 (MPENSGA2 algorithm), a multi-objective evolutionary algorithm, was used to train radial basis function neural networks, where accuracy was the measure used to evaluate model performance, along with the minimum sensitivity measurement. The neural network models obtained from the Pareto fronts were used to develop a rule-based system. This system will help medical experts allocate organs. Results: The models obtained with the MPENSGA2 algorithm generally yielded competitive results for all performance metrics considered in this work, namely the correct classification rate (C), minimum sensitivity (MS), area under the receiver operating characteristic curve (AUC), root mean squared error (RMSE) and Cohen's kappa (Kappa). In general, the multi-objective evolutionary algorithm demonstrated a better performance than the mono-objective algorithm, especially with regard to the MS extreme of the Pareto front, which yielded the best values of MS (48.98) and AUC (0.5659). The rule-based system efficiently complements the current allocation system (model for end-stage liver disease, MELD) based on the principles of efficiency and equity. This complementary effect occurred in 55% of the cases used in the simulation. The proposed rule-based system

关键词： Making decisions rule-based multi-objective evolutionary algorithm Radial basis function neural networks Liver transplantation Organ allocations

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Novel soft subspace clustering with multi-objective evolutionary approach for high-dimensional data

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PATTERN RECOGNITION 2013年第9期46卷 2562-2575页

作者： Xia, Hu Zhuang, Jian Yu, Dehong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Many conventional soft subspace clustering techniques merge several criteria into a single objective to improve performance;however, the weighting parameters become important but difficult to set. In this paper, a novel soft subspace clustering with a multi-objective evolutionary approach (MOEASSC) is proposed to this problem. This clustering method considers two types of criteria as multiple objectives and optimizes them simultaneously by using a modified multi-objective evolutionary algorithm with new encoding and operators. An indicator called projection similarity validity index (PSVIndex) is designed to select the best solution and cluster number. Experiments on many datasets demonstrate the usefulness of MOEASSC and PSVIndex, and show that our algorithm is insensitive to its parameters and is scalable to large datasets. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Subspace clustering multi-objective evolutionary algorithm Determination of the best solution Determination of the cluster number

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Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem

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SOFT COMPUTING 2013年第2期17卷 275-284页

作者： Cruz-Ramirez, M. Hervas-Martinez, C. Gutierrez, P. A. Perez-Ortiz, M. Briceno, J. de la Mata, M. Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain Hosp Reina Sofia CIBERehd Liver Transplantat Unit Cordoba Spain

Donor-recipient matching constitutes a complex scenario difficult to model. The risk of subjectivity and the likelihood of falling into error must not be underestimated. Computational tools for the decision-making process in liver transplantation can be useful, despite the inherent complexity involved. Therefore, a multi-objective evolutionary algorithm and various techniques to select individuals from the Pareto front are used in this paper to obtain artificial neural network models to aid decision making. Moreover, a combination of two pre-processing methods has been applied to the dataset to offset the existing imbalance. One of them is a resampling method and the other is a outlier deletion method. The best model obtained with these procedures (with AUC = 0.66) give medical experts a probability of graft survival at 3 months after the operation. This probability can help medical experts to achieve the best possible decision without forgetting the principles of fairness, efficiency and equity.

关键词： Artificial neural networks Liver transplantation multi-objective evolutionary algorithm Organ allocations Pre-processing

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evolutionary algorithmS AND FUZZY SETS FOR DISCOVERING TEMPORAL RULES

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2013年第4期23卷 855-868页

作者： Matthews, Stephen G. Gongora, Mario A. Hopgood, Adrian A. Univ Bristol Dept Engn Math Intelligent Syst Lab Bristol BS8 1UB Avon England De Montfort Univ Ctr Computat Intelligence Leicester LE1 9BH Leics England Sheffield Hallam Univ Sheffield Business Sch Sheffield S1 1WB S Yorkshire England

A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method's ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

关键词： fuzzy association rules temporal association rules multi-objective evolutionary algorithm

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A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm

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ANNALS OF OPERATIONS RESEARCH 2013年第1期206卷 449-483页

作者： Tavana, Madjid Khalili-Damghani, Kaveh Abtahi, Amir-Reza La Salle Univ Lindback Distinguished Chair Informat Syst & Deci Philadelphia PA 19141 USA Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran Univ Econ Sci Fac Econ Inst Management Dept Knowledge Engn & Decis Sci Tehran Iran

Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy multidimensional multiple-choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

关键词： multidimensional multiple-choice knapsack multi-objective evolutionary algorithm Efficient epsilon-constraint Data envelopment analysis

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A multi-objective Covariance Matrix Adaptation evolutionary Strategy based on Decomposition for Analog Circuit Design

A Multi-Objective Covariance Matrix Adaptation Evolutionary ...

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39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Park, So-Youn Lee, Ju-Jang Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

ISBN: (纸本)9781479902248

Sound localization has been utilized in many fields. For good performance, sound signal obtained from the microphone has to be well filtered and amplified. Although filtering and amplification can be implemented in digital, analog circuit is needed because of sampling aliasing problem and limitation of computational resources in real-time application. The analog circuit consists of several components. Because all of possible combinations cannot be investigated, evolutionary algorithm (EA) is chosen to find promising solution candidates. In the paper, we built a multi-objective covariance adaptation evolutionary strategy based on decomposition to obtain the component values. Through the experimental results, our proposed algorithm showed good performance.

关键词： State variable filter covariance matrix adaptation evolution strategy voltage level shifter circuit optimization multi-objective evolutionary algorithm scalarization

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evolutionary algorithms for a Three-objectives Oil Derivatives Network Problem

Evolutionary Algorithms for a Three-objectives Oil Derivativ...

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IEEE Congress on evolutionary Computation

作者： de Souza, Thatiana C. N. Goldbarg, Elizabeth F. G. Goldbarg, Marco C. Univ Fed Rio Grande do Norte Dept Informat & Appl Math Natal RN Brazil

ISBN: (纸本)9781479904549;9781479904532

To distribute oil derivatives by multi-product pipelines is an important problem faced by the petroleum industry. Some researchers deal with it as a discrete problem where batches of products flow in a network. Minimizing delivery time is a usual objective handled by engineers when scheduling products in pipeline networks. Nevertheless, other costs may also be considered such as losses due to interfaces and electrical energy. Losses due to interfaces occur when different products sent consecutively contaminate each other. Usually, no separation devices exist between batches of different products and losses due to interface can be significant. The price paid for electrical energy varies during the day, so it is important also to try to minimize this cost. These three minimization objectives are considered, simultaneously, i.e. delivery time at demand nodes, interface losses and electrical energy cost. A transgenetic algorithm is proposed and applied to thirty random instances. The results obtained with the proposed method are compared with those produced by an NSGAII algorithm.

关键词： distribution network oil derivatives multi-objective evolutionary algorithm

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A Novel Diversity Maintenance Scheme for evolutionary multi-objective Optimization

A Novel Diversity Maintenance Scheme for Evolutionary Multi-...

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14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Gee, Sen Bong Qiu, Xin Tan, Kay Chen Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9783642412783;9783642412776

Recently, decomposition-based multi-objective evolutionary algorithm (MOEA/D) has received increasing attentions due to its simplicity and decent optimization performance. In the presence of the deceptive optimum, the weight vector approach used in MOEA/D may not be able to prevent the population traps into local optimum. In this paper, we propose a new algorithm, namely Diversity Preservation multi-objective evolutionary algorithm based on Decomposition (DivPre-MOEA/D), which uses novel diversity maintenance scheme to enhance the performance of MOEA/D. The proposed algorithm relaxes the dependency of the weight vector approach on approximated ideal vector to maintain diversity of the population. The proposed algorithm is evaluated on CEC-09 test suite and compared the optimization performance with MOEA/D. The experiment results show that DivPre-MOEA/D can provide better solutions spread along the Pareto front.

关键词： Diversity-preservation multi-objective evolutionary algorithm decomposition

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