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IEEE International Conference on Systems, Man, and Cybernetics

作者： Yu Liu Wei-Neng Chen Zhi-Hui Zhan Ying Lin Yue-Jiao Gong Jun Zhang Department of Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Sensor Network Ministry of Education Key Laboratory of Software Technology Education Department of Guangdong Province P.R. China

ISBN: (纸本)9781479906505

The TSP problem is considered as classical discrete optimization grouping problem, which is widely used in practice, but it is real a difficult NP problem. Simultaneously differential evolution (DE) algorithm has been proven to be a powerful optimization algorithm. Since the mutation process of DE contains a series of arithmetic operators operating on continuous space, few algorithms based on DE solve this problem nicely and the advantages of DE in continuous space cannot be used to solve TSP. To take full advantages of the strengths of DE, this paper proposes a set-based DE (S-DE) which completely follows the procedure of the original DE. We present a representation scheme to characterize the discrete problem space and by redefining its basic concept and all related operators in mutation, DE can operate directly on the original set space of the discrete optimization problems instead of performing a space transformation. In that way, the searching features of DE in continuous space is kept. In experiment, we test the performance of our proposed S-DE and the results show it is very promising.

关键词： component Discrete differential algorithm Sebased COP TSP MT CARD16 gene discrete element Optimization algorithms Total suspended particles evolutionary algorithm arithmetic operator NP problem CONTINUOUS SPACE differential evolution algorithms

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Feature Selection by differential evolution Algorithm - A Case Study in Personnel Identification

Feature Selection by Differential Evolution Algorithm - A Ca...

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IEEE Congress on evolutionary Computation

作者： Kingshuk Chakravarty Diptesh Das Aniruddha Sinha Amit Konar Innovation Lab Tata Consultancy Services Ltd. Electronics & Telecommunication Engineering Dept. Jadavpur University

ISBN: (纸本)9781479904532

Feature selection is an important area of research as it has a tremendous effect on the accuracy and performance of classification algorithms. In this paper we propose an objective function for feature selection, which combines the intra class feature variation and inter class feature distance using a Lagrangian multiplier. The inter class distance is measured using the sum of absolute difference of the ratio of mean and standard deviation for respective classes. The objective function is minimized using differential evolutionary (DE) Algorithm where the population vector is encoded using Binary Encoded Decimal to avoid the float number optimization problem. An automatic clustering of the possible values of the Lagrangian multiplier provides a detailed insight of the selected features during the proposed DE based optimization process. The classification accuracy of Support Vector Machine (SVM) is used to measure the performance of the selected features. The proposed algorithm outperforms the existing DE based approaches when tested on IRIS, Wine, Wisconsin Breast Cancer, Sonar and Ionosphere datasets. The same algorithm when applied on gait based people identification, using skeleton datapoints obtained from Microsoft Kinect sensor, exceeds the previously reported accuracies.

关键词： Feature extraction MT Lagrangian Objective function evolutionary algorithm differential evolution algorithms absolute difference Optimization Procedure Support Vector Network Multiplying circuits Case studies

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A novel Two-layer Hierarchical differential evolution Algorithm for Global Optimization

A novel Two-layer Hierarchical Differential Evolution Algori...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Yinzhi Zhou Xinyu Li Liang Gao State Key Laboratory of Digital Manufacturing Equipment &Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781479906505

This paper proposes a novel Two-layer Hierarchical differential evolution (THDE) algorithm to improve the search ability of differential evolution (DE) algorithm. Individuals are separated into bottom layer and top layer. In the bottom layer, individuals are divided into several groups. Modified DE/current-best/1/bin strategy is conducted to produce offspring, where the best individual comes from top layer. In the top layer, modified DE/rand/1/bin strategy is used to update individuals. A set of famous benchmark functions has been used to test and evaluate the performance of the proposed THDE. The experimental results show that the proposed algorithm is better than DE/current-best/1/bin and DE/rand/1/bin and better than or at least comparable to the self-adaptive DE (JDE) and intersect mutation differential evolution algorithm (IMDE) for most functions.

关键词： differential evolution two-layer hierarchy global optimization Global optimization top layer differential evolution algorithms TWO-LAYER evolutionary algorithm Hierarchical MT

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Modeling the Term Structure of Government Bond Yields with a differential evolution Algorithm

Modeling the Term Structure of Government Bond Yields with a...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Maciel, Leandro Gomide, Fernando Ballini, Rosangela Univ Estadual Campinas Sch Elect & Comp Engn Dept Comp Engn & Ind Automat Campinas SP Brazil Univ Estadual Campinas Inst Econ Dept Econ Theory Campinas Brazil

ISBN: (纸本)9781467318037

Modeling the term structure of government bond yields is of major interest to macroeconomists and financial market practitioners. It is crucial for bonds and derivatives pricing, risk management, revealing market expectations, and essential for monetary policy decisions. This paper proposes the use of differential evolution to estimate the term structure of government bond yields considering parsimonious modeling. differential evolution avoids non-convergence problems and the high instability of model parameter estimation caused by the need to choose initial values by the curve fitting algorithms of traditional optimization methods. Results show that differential evolution algorithm gives more accurate yield curves than nonlinear least squares and genetic algorithms, highlighting its potential as a tool for term structure of interest rates estimation.

关键词： differential evolution algorithms Government Nonlinear least squares Modeling evolutionary algorithm

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Sidelobe Level and Sideband Optimization for Thinned Planar Antenna Arrays Using Time Modulation

Sidelobe Level and Sideband Optimization for Thinned Planar ...

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European Conference on Antennas and Propagation

作者： Erkan Afacan Department of Electrical and Electronics Engineering Faculty of Engineering Gazi University Maltepe Ankara/Turkey

In this work, time modulation and differential evolution algorithm is applied to thinned planar antenna arrays. It is shown that it is possible to obtain even lower sidelobe levels and to maintain sideband radiation b... 详细信息

ISBN: (纸本)9781467321877

关键词： Planar antenna arrays Array thinning Time modulation differential evolution time modulation planar antenna arrays side lobe level Antenna arrays differential evolution algorithms

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Intelligent Garbage Can Decision-Making Model evolution Algorithm for optimization of structural topology of plane trusses

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APPLIED SOFT COMPUTING 2012年第9期12卷 2719-2727页

作者： Kuo, Hsin-Chuan Chiu, Jinn-Tong Lin, Ching-Hai De Lin Inst Technol Dept Mech Engn Taipei Taiwan Natl Taiwan Ocean Univ Dept Syst Engn & Naval Architecture Taipei Taiwan

The optimum design of structural topology of trusses is widely acknowledged as the most difficult and challenging problem in the area of structural optimization. Based on differential evolution algorithms and using the framework of a Garbage Can Decision-Making Model, we proposed an Intelligent Garbage Can Decision-Making Model evolution Algorithm (IGCMEA) to simulate the decision-making process in human social organizations. In a decision-making process, when faced with issues such as unclear goals and methods, employee turnover and so forth, representatives of all participating parties will communicate, argue, compromise and adapt with each other in order to find a solution to the problems. Group meetings are conducted to choose the best solution in a more objective, reasonable and efficient manner. By applying the differential evolution (DE) algorithm and IGCMEA to perform an optimization test on the 100-dimensional Schwefel Function, we showed that IGCMEA can achieve an efficient and satisfactory result. We also optimized the truss topology using IGCMEA and obtained a better result than when using the Genetic Algorithm as in the literature, thus illustrating the superior power of IGCMEA. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Optimum design differential evolution algorithms Garbage Can Decision-Making Model Topology optimization

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Offline Handwritten Signature Verification through Network Radial Basis Functions optimized by differential evolution

Offline Handwritten Signature Verification through Network R...

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International Joint Conference on Neural Networks

作者： Saulo Henrique Leoncio de Medeiros Napoles Cleber Zanchettin Informatic Center Federal University of Pernambuco

ISBN: (纸本)9781467314886

The handwritten signature is present in all important documents. In law, if the signature on a document is false, this document is also considered a fraud. This paper uses a neural network of radial basis function optimized by differential evolution Algorithm with features that best discriminates between a genuine signature of a simulated forgery. The experiments with this promising technique were made with a GPDS-300gray images base and the results subjected to statistical tests with the performance of technical literature.

关键词： Component Handwritten Signature Off-line Verification Radial Basis Function differential evolution Radial basis function basis functions handwritten signature differential evolution algorithms forgery Statistical tests OFF LINE signatures Neural network evolutionary algorithm

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Multi-objective test suite minimisation using Quantum-inspired Multi-objective differential evolution Algorithm

Multi-objective test suite minimisation using Quantum-inspir...

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IEEE International Conference on Computational Intelligence and Computing Research

作者： A. Charan Kumari K. Srinivas M. P. Gupta Department of Physics & Computer Science Dayalbagh Educational Institute (Deemed University) Agra India Department of Electrical Engineering Dayalbagh Educational Institute (Deemed University) Agra India Department of Management Studies Indian Institute of Technology New Delhi India

ISBN: (纸本)9781467313421

This paper presents the solution for multi-objective test suite minimisation problem using Quantum-inspired Multi-objective differential evolution Algorithm. Multi-objective test suite minimisation problem is to select a set of test cases from the available test suite while optimizing the multi objectives like code coverage, cost and fault history. As test suite minimisation problem is an instance of minimal hitting set problem which is NP-complete; it cannot be solved efficiently using traditional optimization techniques especially for the large problem instances. This paper presents Quantum-inspired Multi-objective differential evolution Algorithm (QMDEA) for the solution of multi-objective test suite minimisation problem. QMDEA combines the preeminent features of differential evolution and Quantum Computing. The features of QMDEA help in achieving quality Pareto-optimal front solutions with faster convergence. The performance of QMDEA is tested on two real world applications and the results are compared against the state-of-the-art multi-objective evolutionary algorithm NSGA-II. The comparison of the obtained results indicates superior performance of QMDEA.

关键词： regression testing software testing test suite minimisation regression testing program testing CASING INSPECTION differential Solutions differential evolution algorithms evolution evolution

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Optimization and Design of a Wideband Vertically Stacked Yagi-Uda Antenna Array with a Wide Azimuth HPBW

Optimization and Design of a Wideband Vertically Stacked Yag...

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10th International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yuan, Jiantao Fu, Guang Lei, Juan Luo, Yong Xidian Univ Natl Key Lab Antennas & Microwave Technol Xian 710071 Peoples R China

ISBN: (纸本)9781467318006

A vertically stacked Yagi-Uda antenna array with X-shape driven dipoles for the long range Meteor Burst Communication(MBC) at the VHF is presented using differential evolution algorithm (DE). In order to illustrate the capabilities of the method, a 3-element Yagi-Uda antenna array is given. The measured and simulated impedance bandwidth of VSWR<1.4 reaches 22% and some solved methods are proposed to cope with the demand from wide azimuth HPBW. The results show that the simulated antenna gain across the operating frequency band is 8.1 similar to 8.3dB and the HPBW in the azimuth plane is above 90 degrees.

关键词： Yagi antenna azimuth plane operating frequency band azimuth antenna gain differential evolution algorithms dipoles broadband

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A Density evolution Based Framework for Dirty Paper Code Design Using TCQ and Multilevel LDPC Codes

A Density Evolution Based Framework for Dirty Paper Code Des...

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IEEE International Conference on Communications (ICC)

作者： Yang, Yang Xiong, Zixiang Wu Yu-chun Zhang, Philipp Texas A&M Univ Dept ECE College Stn TX 77843 USA Huawei Hisi Adv Res Dept Beijing Peoples R China

ISBN: (纸本)9781457720536

We propose a density evolution based dirty-paper code design framework that combines trellis coded quantization with multi-level low-density parity-check (LDPC) codes. Unlike existing design techniques based on Gaussian approximation and EXIT charts, the proposed framework tracks the empirically collected log-likelihood ratio (LLR) distributions at each iteration, and employs density evolution and differential evolution algorithms to design each LDPC component code. In order for the approximated LLR distributions obtained using density evolution to better match the true LLR distributions, a novel decorrelator is added to the decoder to make channel LLRs and check node LLRs almost independent of each other. Simulation results show that at 1 bit per sample transmission rate, the dirty-paper codes designed using the proposed method operate within 0.53 dB of the theoretical limit, reducing the best known result (with a 0.58 dB gap to the limit) by 0.05 dB at the same complexity and block length, while this improvement can be as large as 0.21 dB (corresponding to a 0.37 dB gap to the limit) when we use higher complexity encoder/decoder with longer block length.

关键词： low density parity check codes dubnium decibel Codes differential evolution algorithms Frameworks trellis coded quantization EXITS Gaussian approximation evolution evolution evolution evolution decoders

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