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4th International Conference on Swarm, evolutionary, and Memetic Computing (SEMCCO)

作者： Pati, Soumen Kumar Das, Asit Kumar Ghosh, Arka St Thomas Coll Engn & Technol Dept Comp Sci Informat Technol Kolkata 700023 India Bengal Engn & Sci Univ Dept Comp Sci & Technol Sibpur 711103 Howrah India Bengal Engn & Sci Univ Purabi Sch Informat Technol Sibpur 03 Howrah India

ISBN: (纸本)9783319037554;9783319037561

Feature selection is one of the most key problems in the field of machine learning and data mining. It can be done in mainly two different ways, namely, filter approach and wrapper approach. Filter approach is independent of underlying classifier logic and relatively less costly than the wrapper approach which is classifier dependent. Many researchers have applied Genetic algorithm (GA) as wrapper approach for feature selection. In the paper, a novel feature selection method is proposed based on the multi-objective genetic algorithm which is applied on population generated by non-linear uniform hybrid cellular automata. The fitness functions are defined one using set lower bound approximation of rough set theory and the other using Kullbak-Leibler divergence method. A comparative study between proposed method and some leading feature selection methods are given using some popular microarray cancer dataset to demonstrate the effectiveness of the method.

关键词： Feature selection Genetic algorithm multi-objective evolutionary algorithm Set lower bound approximation Kullback-Leibler divergence

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Robustness Analysis of a MOEA-based Elicitation Method for Outranking Model Parameters

Robustness Analysis of a MOEA-based Elicitation Method for O...

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10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Covantes, Edgar Fernandez, Eduardo Navarro, Jorge Autonomous Univ Sinaloa Fac Informat Sci Culiacan Sinaloa Mexico

ISBN: (纸本)9781479914609

To set the parameter values for the outranking model is usually a demanding task for the decision-maker (DM) because it is necessary to provide a large number of parameters (thresholds and weights). The use of indirect methods (preference-disaggregation analysis (PDA)) to infer the set of parameters from a battery of decision examples allows the DM to avoid the task of specifying these values. In this paper a robustness analysis has been made to a PDA method based on an evolutionary approach. In this case we use THESEUS method as an artificial DM and its assignment rule for evaluating each individual of a multi-objective evolutionary algorithm (MOEA) population. The non-dominated solutions are acceptable parameter settlements.

关键词： Robustness analysis preference-disaggregation analysis THESEUS method multi-objective evolutionary algorithm

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Internet Traffic Classification using MOEA and Online Refinement in Voting on Ensemble Methods

Internet Traffic Classification using MOEA and Online Refine...

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21st Iranian Conference on Electrical Engineering (ICEE)

作者： Aliakbarian, Mohammad Sadegh Fanian, Ali Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

ISBN: (纸本)9781467356343

Internet Traffic Classification is one of the most important issues in network management. Until now, many methods have been proposed to this end, but studies show that machine learning based algorithms have a good performance in comparison to other methods. Selecting the best feature subset causes better accuracy and efficiency in machine learning algorithms. In this paper, in order to obtain higher classification accuracy, effective features are selected using a multi-objective evolutionary algorithm. In this evolutionary algorithm, some objectives such as minimizing feature number, maximizing classification accuracy, maximizing true positive rate (maximizing TPR), and minimizing false positive rate (minimizing FPR) are satisfied simultaneously and with no conflicts with each other. In the proposed method, selected features subset is given to a new ensemble algorithm with online refinement during training and testing phases. Final result of each new ensemble algorithm is obtained by the vote of the majority with respect to the accuracy of any voter. Results show the high efficiency and performance of proposed method in comparison with other methods. So that the WWW traffic classification accuracy ascend to 99.93%. The results for 8 other traffics such as P2P indicate high accuracy of the proposed method.

关键词： Traffic Classification multi-objective evolutionary algorithm Ensemble method

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Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms

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APPLIED SOFT COMPUTING 2012年第1期12卷 28-39页

作者： Nandi, Arup Kumar Deb, Kalyanmoy Ganguly, Subhas Datta, Shubhabrata Cent Mech Engn Res Inst CSIR CMERI Durgapur 713209 W Bengal India Indian Inst Technol Kanpur Dept Mech Engn Kanpur 208016 Uttar Pradesh India Natl Inst Technol Dept Met Engn Raipur 492010 CG India Birla Inst Technol Deoghar 814142 Jharkhand India

To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of various metallic fillers in particulate reinforced flexible mould material composites, multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Here we have adopted two EA-based algorithms namely NSGAII and SPEA2 in order to solve the present problem independently. Comparative study of the results reveals that NSGAII performs better over SPEA2 for investigating the role of metallic fillers in particulate reinforced flexible mould material composites. A recently proposed innovization procedure is also used to unveil salient properties associated with the obtained trade-off solutions. These solutions are analyzed to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mould material. Based on the findings through investigations, the optimal selection of materials is suggested including the cost implication factor. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Particle reinforced flexible mould material composites Metallic filler multi-objective evolutionary algorithm Soft tooling process Reducing cooling time

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Encouraging Behavioral Diversity in evolutionary Robotics: An Empirical Study

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evolutionary COMPUTATION 2012年第1期20卷 91-133页

作者： Mouret, J. -B. Doncieux, S. Univ Paris 06 CNRS UMR 7222 ISIR F-75252 Paris 05 France

evolutionary robotics (ER) aims at automatically designing robots or controllers of robots without having to describe their inner workings. To reach this goal, ER researchers primarily employ phenotypes that can lead to an infinite number of robot behaviors and fitness functions that only reward the achievement of the task and not how to achieve it. These choices make ER particularly prone to premature convergence. To tackle this problem, several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation;however, it also introduces new questions: how to compare behavior? should this comparison be task specific? and what is the best way to encourage diversity in this context? In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework. We compare each approach on three different tasks and two different genotypes. The results show that fostering behavioral diversity substantially improves the evolutionary process in the investigated experiments, regardless of genotype or task. Among the benchmarked approaches, multi-objective methods were the most efficient and the generic, Hamming-based, behavioral distance was at least as efficient as task specific behavioral metrics.

关键词： evolutionary robotics diversity multi-objective evolutionary algorithm neural networks

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multi-objective design of UWB antennas using surrogate-based optimization

Multi-objective design of UWB antennas using surrogate-based...

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IEEE International Symposium on Antennas and Propagation

作者： S. Koziel S. Ogurtsov Eng. Optimization & Modeling Center Reykjavik Univ. Reykjavik Iceland

ISBN: (纸本)9781467353168

A procedure for multi-objective design of ultra-wideband (UWB) antennas is presented. The goal is to reduce the antenna size while ensuring that the matching requirements are satisfied. Our approach exploits the multi-objective evolutionary algorithm (MOEA) working with a fast surrogate model of the antenna obtained with kriging interpolation of coarse-discretization simulation data. Response correction techniques are subsequently applied to refine the designs obtained by MOEA. Our methodology allows us to obtain-at a low computational cost-a set of designs corresponding to various trade-offs between antenna size and matching.

关键词： Ultra wideband antennas ANTENNA Kriging interpolation multi-objective optimal design multi-objective evolutionary algorithm

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A multi-objective Differential evolutionary algorithm Based on Spacial Distance

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3rd International Conference on Intelligence Computation and Applications

作者： Zheng, Jinhua Wu, Jun Lv, Hui Xiangtan Univ Inst Informat Engn Xiangtan 411105 Peoples R China

ISBN: (纸本)9783540921363

Differential Evolution (DE) is a relatively new EA and it has become a major branch of EA. Based on the classical Differential Evolution. we proposed a new multi-objective Differential evolutionary algorithm ill this paper. Compared with NSGA-II and epsilon-MOEA, the experimental results demonstrate that the new algorithm tends to be more effective in obtaining good convergence and can converge to the true Pareto front with comparable efficiency.

关键词： multi-objective evolutionary algorithm multi-objective optimal problem Differential evolutionary Spacial Distance

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Data Clustering Using multi-objective Hybrid evolutionary algorithm

Data Clustering Using Multi-Objective Hybrid Evolutionary Al...

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International Conference on Control, Automation and Systems

作者： Won, Jin-Myung Ullah, Sami Karray, Fakhreddine Voice Enabling Syst Technol Inc Waterloo ON Canada Dept Elect & Comp Engn Waterloo ON Canada Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9788995003893

This paper proposes a multi-objective evolution strategy (ES) hybridized with a k-means algorithm to address a data clustering problem whose objective is minimizing both clustering error and cluster number. Contrary to the conventional data clustering problem with a predetermined number of clusters, the bi-objective problem considered in this study has a set of clustering solutions whose cluster numbers are different from one another. This enables to secure the best clustering result that fits specific needs without restricting the cluster number. To find the solution set, the hybrid ES evolves a population of solution candidates each of which represents a variable number of cluster centroids. While evolving the population, special ES operators dedicated to the bi-objective clustering problem are used. Whenever the hybrid ES creates a new set of cluster centroids, it is fine-tuned by the k-means algorithm. The experiment results show that the hybrid ES outperforms the conventional ES and KMA.

关键词： Clustering multi-objective evolutionary algorithm k-means algorithm

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Risk conscious solution of planning problems under uncertainty by hybrid multi-objective evolutionary algorithms

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COMPUTERS & CHEMICAL ENGINEERING 2011年第11期35卷 2521-2539页

作者： Tometzki, Thomas Engell, Sebastian Tech Univ Dortmund Dept Biochem & Chem Engn Proc Dynam & Operat Grp D-44227 Dortmund Germany

We consider the risk conscious solution of planning problems with uncertainties in the problem data. The problems are formulated as two-stage stochastic mixed-integer models in which some of the decisions (first-stage) have to be made under uncertainty and the remaining decisions (second-stage) can be made after the realization of the uncertain parameters. The uncertain model parameters are represented by a finite set of scenarios. The risk conscious optimization problem under uncertainty is solved by a stage decomposition approach using a multi-objective evolutionary algorithm which optimizes the expected scenario costs and the risk criterion with respect to the first-stage decisions. The second-stage scenario decisions are handled by mathematical programming. Results from numerical experiments for two real-world problems are shown. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Planning under uncertainty Risk conscious planning Stage decomposition Hybrid algorithm multi-objective evolutionary algorithm

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A coordinated voltage/reactive power control method for multi-TSO power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 20-28页

作者： Zhang, Anan Li, Hongwei Liu, Fan Yang, Honggeng SW Petr Univ Sch Elect Engn & Informat Chengdu 610500 Peoples R China Sichuan Elect Power Res Inst Chengdu 610000 Peoples R China Sichuan Univ Sch Elect Engn & Informat Chengdu 610065 Peoples R China

multi area power systems work most often with a poor inter-regional coordination about reactive power concerns. Poor coordinated operation may not achieve significant improvements in the quality and efficiency of power system operation, and may even increase the risk of blackout for multi-TSO (Transmission System Operators) power systems. This paper focused on the voltage/reactive power coordinated control. Voltage/reactive power interactions between interconnected power networks were derived from the augmented active and reactive power decoupled network equations. According to distribution computing concept, a novel voltage/reactive power control model was presented in this paper, which could optimize the active power losses of both local network and its interconnected areas. And the model's data communication was simple: only an equivalent susceptance matrix and the optimal reactive power injection value for external network need be communicated irregularly. Moreover, this model could avoid raising confidentiality issues because it need not exchange explicit structure and constraints information between different TSOs. Efficacy of the proposed model was illustrated through simulations on two IEEE systems and an application to an actual system. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Voltage control Reactive power control Coordinated control Active and reactive power decoupled network equations multi-objective evolutionary algorithm

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