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7th International Conference on Swarm Intelligence (ICSI)

作者： Sopov, Evgenii Siberian State Aerosp Univ Dept Syst Anal & Operat Res Krasnoyarsk Russia

ISBN: (纸本)9783319410005;9783319409993

In recent years many real-world optimization problems have had to deal with growing dimensionality. Optimization problems with many hundreds or thousands of variables are called large-scale global optimization (LGSO) problems. The most advanced algorithms for LSGO are proposed for continuous problems and are based on cooperative coevolution schemes using the problem decomposition. In this paper a novel technique is proposed. A genetic algorithm is used as the core technique. The estimation of distribution algorithm is used for collecting statistical data based on the past search experience to provide the problem decomposition by fixing genes in chromosomes. Such an EDA-based decomposition technique has the benefits of the random grouping methods and the dynamic learning methods. The results of numerical experiments for benchmark problems from the CEC' 13 competition are presented. The experiments show that the approach demonstrates efficiency comparable to other advanced algorithms.

关键词： estimation of distribution algorithm Genetic algorithm Large-scale global optimization Problem decomposition

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An Effective DE-EDA for Permutation Flow-shop Scheduling Problem

An Effective DE-EDA for Permutation Flow-shop Scheduling Pro...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Li, Zuo-cheng Guo, Qingxin Tang, Lixin Northeastern Univ Inst Ind Engn & Logist Optimizat Shenyang Peoples R China

ISBN: (纸本)9781509006229

Aiming at the permutation flow-shop scheduling problem (PFSSP) with makespan criterion, a combination algorithm based on differential evolution (DE) and estimation of distribution algorithm (EDA), namely DE-EDA, is proposed. Firstly, DE-EDA combines the probability-dependent macro information extracted by EDA and the individual-dependent micro information obtained by DE to execute the exploration, which is helpful in guiding the global search to explore promising solutions. Secondly, in order to make DE well suited to solve PFSSP, a convert rule named smallest-ranked-value (SRV) is designed to generate the discrete job permutations from the continuous values. Thirdly, a sequence-learning-based Bayes posterior probability is presented to estimate EDA's probability model and sample new solutions, so that the global information of promising search regions can be learned precisely. In addition, a simple but effective two-stage local search is embedded into DE-EDA to perform the exploitation, and thereafter numerous potential solution(s) with relative better fitness can be exploited in some narrow search regions. Finally, simulation experiments and comparisons based on 29 well-known benchmark instances demonstrate the effectiveness of the proposed DE-EDA.

关键词： differential evolution estimation of distribution algorithm probability model permutation flow-shop scheduling problem makespan

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EDA-based Decomposition Approach for Binary LSGO Problems 8

EDA-based Decomposition Approach for Binary LSGO Problems

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8th International Joint Conference on Computational Intelligence

作者： Sopov, Evgenii Siberian State Aerosp Univ Dept Syst Anal & Operat Res Krasnoyarsk Russia

ISBN: (纸本)9789897582011

In recent years many real-world optimization problems have had to deal with growing dimensionality. Optimization problems with many hundreds or thousands of variables are called large-scale global optimization (LSGO) problems. Many well-known real-world LSGO problems are not separable and are complex for detailed analysis, thus they are viewed as the black-box optimization problems. The most advanced algorithms for LSGO are based on cooperative coevolution schemes using the problem decomposition. These algorithms are mainly proposed for the real-valued search space and cannot be applied for problems with discrete or mixed variables. In this paper a novel technique is proposed, that uses a binary genetic algorithm as the core technique. The estimation of distribution algorithm (EDA) is used for collecting statistical data based on the past search experience to provide the problem decomposition by fixing genes in chromosomes. Such an EDA-based decomposition technique has the benefits of the random grouping methods and the dynamic learning methods. The EDA-based decomposition GA using the island model is also discussed. The results of numerical experiments for benchmark problems from the CEC competition are presented and discussed. The experiments show that the approach demonstrates efficiency comparable to other advanced techniques.

关键词： Large-Scale Global Optimization Problem Decomposition estimation of distribution algorithm Binary Genetic algorithm

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Genetic & Evolutionary Biometrics: Hybrid Feature Selection and Weighting for a Multi-Modal Biometric System

Genetic & Evolutionary Biometrics: Hybrid Feature Selection ...

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IEEE SoutheastCon

作者： Alford, Aniesha Steed, Crystal Jeffrey, Marcus Sweet, Donovan Shelton, Joseph Small, Lasanio Leflore, Derrick Dozier, Gerry Bryant, Kelvin Kelly, John C. Abegaz, Tamirat Ricanek, Karl N Carolina Agr & Tech State Univ Ctr Adv Studies Ident Sci Greensboro NC 27411 USA Clemson Univ Comp Sci Clemson SC USA North Carolina A&T State Univ Elect & Comp Engn Greensboro NC USA Univ N Carolina Ctr Adv Studies Ident Sci Wilmington NC USA

ISBN: (纸本)9781467313759

The Genetic & Evolutionary Computation (GEC) research community is seeing the emergence of a new and exciting subarea, referred to as Genetic & Evolutionary Biometrics (GEB), as GECs are increasingly being applied to a variety of biometric problems. In this paper, we present successful GEB techniques for multi-biometric fusion and multi-biometric feature selection and weighting. The first technique, known as GEF (Genetic & Evolutionary Fusion), seeks to optimize weights for score-level fusion. The second technique is known as GEFeWSML (Genetic & Evolutionary Feature Weighting and Selection-Machine Learning). The goal of GEFeWSML is to evolve feature masks (FMs) that achieve high recognition accuracy, use a low percentage of features, and generalize well to unseen subjects. GEFeWSML differs from the other GEB techniques for feature selection and weighting in that it incorporates cross validation in an effort to evolve FMs that generalize well to unseen subjects.

关键词： Biometrics Cross Validation estimation of distribution algorithm Feature Selection Feature Weighting Genetic & Evolutionary Computation Local Binary Pattern

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estimation of Bayesian networks algorithms in a class of complex networks

Estimation of Bayesian networks algorithms in a class of com...

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2010 IEEE World Congress on Computational Intelligence

作者： Echegoyen, Carlos Mendiburu, Alexander Santana, Roberto Lozano, Jose A. Univ Basque Country Intelligent Syst Grp Paseo Manuel de Lardizabal 1 San Sebastian 20080 Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9781424481262

In many optimization problems, regardless of the domain to which it belongs, the structural component that the interactions among variables provides can be seen as a network. The impact that the topological characteristics of that network has, both in the hardness of the problem and in the performance of the optimization techniques, constitutes a very important subject of research. In this paper, we study the behavior of estimation of distribution algorithms (EDAs) in functions whose structure is defined by using different network topologies which include grids, small-world networks and random graphs. In order to do that, we use several descriptors such as the population size, the number of evaluations as well as the structures learned during the search. Furthermore, we take measures from the field of complex networks such as clustering coefficient or characteristic path length in order to quantify the topological properties of the function structure and analyze their relation with the behavior of EDAs. The results show that these measures are useful to have better understanding of this type of algorithms which have exhibited a high sensitivity to the topological characteristics of the function structure. This study creates a link between EDAs based on Bayesian networks and the emergent field of complex networks.

关键词： Bayesian networks belief networks characteristic path length clustering coefficient complex networks estimation of distribution algorithm function structure network theory (graphs) optimisation optimization problem random graphs small-world networks topological characteristics

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An evolutionary approach for achieving scalability with general regression neural networks

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Natural Computing 2009年第1期8卷 133-148页

作者： Casey, Kenan Garrett, Aaron Gay, Joseph Montgomery, Lacey Dozier, Gerry Department of Computer Science and Software Engineering Auburn University Auburn AL United States

In this paper, we present an approach to overcome the scalability issues associated with instance-based learners. Our system uses evolutionary computational techniques to determine the minimal set of training instances needed to achieve good classification accuracy with an instance-based learner. In this way, instance-based learners need not store all the training data available but instead store only those instances that are required for the desired accuracy. Additionally, we explore the utility of evolving the optimal feature set used by the learner for a given problem. In this way, we attempt to deal with the so-called "curse of dimensionality" associated with computational learning systems. To these ends, we introduce the Evolutionary General Regression Neural Network. This design uses an estimation of distribution algorithm to generate both the optimal training set as well as the optimal feature set for a general regression neural network. We compare its performance against a standard general regression neural network and an optimized support vector machine across four benchmark classification problems. © Springer Science+Business Media B.V. 2007.

关键词： estimation of distribution algorithm Evolutionary computation General regression neural network Support vector machine

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Random Walk Mutation-based DE with EDA for Nonlinear Equations Systems

Random Walk Mutation-based DE with EDA for Nonlinear Equatio...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Liao, Zuowen Gong, Wenyin Cai, Zhihua Wang, Ling Wang, Yong China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781728121536

Finding multiple roots of nonlinear equations systems (NESs) in a single run is an important yet difficult task. It requires to keep a balance between explorative and exploitative traits. In this paper, we present a random walk mutation-based differential evolution (DE) with estimation of distribution algorithm (EDA) to address this problem. The major characteristics are: i) the random walk mutation is capable of preserving the population diversity, which guides individuals to move toward different promising regions;ii) probability selection is employed to provide suitable parent individuals for evolution;iii) EDA is used to accelerate the convergence and obtains the roots. To evaluate the performance of our approach, 30 NESs with diverse features are selected as test suite. Experimental results indicate that the proposed approach is able to yield better performance compared with other state-of-the-art methods.

关键词： Nonlinear equations systems random walk mutation differential evolution estimation of distribution algorithm

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A Generalized Differential Evolution Combined with EDA for Multi-objective Optimization Problems

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4th International Conference on Intelligent Computing

作者： Chen, Wang Shi, Yan-jun Teng, Hong-fei Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

ISBN: (纸本)9783540859833

This paper proposed a multi-objective evolutionary algorithm (called by GDE-EDA hereinafter). The proposed algorithm combined a generalized differential evolution (DE) with an estimation of distribution algorithm (EDA). This combination can simultaneously use global information of population extracted by EDA and differential information by DE. Thus, GDE-EDA can obtain a better distribution of the solutions by EDA while keeping the fast convergence exhibited by DE. The experimental results of the proposed GDE-EDA algorithm were reported on a suit of widely used test functions, and compared with GDE and NSGA-II in the literature.

关键词： Generalized differential evolution estimation of distribution algorithm multi-objective optimization

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Transmission Expansion Planning Based on Hybrid EDA/DE algorithm Considering Wind Power Penetration

Transmission Expansion Planning Based on Hybrid EDA/DE Algor...

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PowerTech, 2015 IEEE Eindhoven

作者： Liu, Wenxia Li, He Xu, Huiting Zhang, Jianhua Zheng, Dehua NCEPU Sch Elect & Elect Engn Beijing Peoples R China Goldwind Beijing Etechwin Elect CO LTD State Wind Power Engn Technol Res Ctr Beijing Peoples R China

ISBN: (纸本)9781479976959

It poses high requirements for the calculation speed and the precision of the solving method when we consider the large-scale transmission expansion planning (TEP) problems. Therefore, combined with the respective characteristics of EDA (distribution of estimation algorithm) and DE (Differential Evolution algorithm), this paper puts forward a new hybrid EDA/DE algorithm for large-scale TEP problems. Meanwhile, it improves the updating mechanism of probabilistic model of EDA based on the characteristics of the TEP problems. Considering the investments of grid company, the new energy incentive politics and network security constraints, this paper proposes a multi-objective static planning model for the TEP considering wind power penetration, which takes the comprehensive cost, the wind curtailment and the risk value into consideration. Finally, a specific example is applied in this paper to verify the applicability and effectiveness of the proposed model and algorithm.

关键词： Differential Evolution estimation of distribution algorithm Transmission Expansion Planning Wind Power Penetration

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A multi-Gaussian component EDA with restarting applied to direction of arrival tracking

A multi-Gaussian component EDA with restarting applied to di...

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IEEE Congress on Evolutionary Computation

作者： Goncalves, Andre R. Boccato, Levy Attux, Romis Von Zuben, Fernando J. Univ Campinas Unicamp Sch Elect & Comp Engn Campinas SP Brazil

ISBN: (纸本)9781479904549;9781479904532

This paper analyzes the application of a multi-population Gaussian-based estimation of distribution algorithm equipped with a restarting strategy and mutation, named MGcEDA, to the problem of estimating the Direction of Arrival (DOA) of time-varying plane waves impinging on a uniform linear array of sensors. This problem requires the minimization of a dynamic cost function which is non-linear, non-quadratic, multimodal and variant with respect to the signal-to-noise ratio. Experiments showed that MGcEDA was able to quickly respond to changes in the source features in scenarios with different levels of noise and number of signals. Moreover, MGcEDA outperforms a previously proposed approach in all considered experiments in terms of well known performance measures.

关键词： Optimization in dynamic environments estimation of distribution algorithm Direction of Arrival estimation

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