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检索条件"主题词=estimation of distribution algorithm"

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Two approaches of using heavy tails in high dimensional EDA 14

Two approaches of using heavy tails in high dimensional EDA

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Sanyang, Momodou L. Muehlbrandt, Hanno Kaban, Ata Univ Birmingham Sch Comp Sci Edgbaston B15 2TT W Midlands England

ISBN: (纸本)9781479942749

We consider the problem of high dimensional black-box optimisation via estimation of distribution algorithms (EDA). The Gaussian distribution is commonly used as a search operator in most of the EDA methods. However there are indications in the literature that heavy tailed distributions may perform better due to their higher exploration capabilities. Univariate heavy tailed distributions were already proposed for high dimensional problems. In 2D problems it has been reported that a multivariate heavy tailed (such as Cauchy) search distribution is able to blend together the strengths of multivariate modelling with a high exploration power. In this paper, we study whether a similar scheme would work well in high dimensional search problems. To get around of the difficulty of multivariate model building in high dimensions we employ a recently proposed random projections (RP) ensemble based approach which we modify to get samples from a multivariate Cauchy using the scale-mixture representation of the Cauchy distribution. Our experiments show that the resulting RP-based multivariate Cauchy EDA consistently improves on the performance of the univariate Cauchy search distribution. However, intriguingly, the RP-based multivariate Gaussian EDA has the best performance among these methods. It appears that the highly explorative nature of the multivariate Cauchy sampling is exacerbated in high dimensional search spaces and the population based search loses its focus and effectiveness as a result. Finally, we present an idea to increase exploration while maintaining exploitation and focus by using the RP-based multivariate Gaussian EDA in which the RP matrices are drawn with i.i.d. heavy tailed entries. This achieves improved performance and is competitive with the state of the art.

关键词： Gaussian distribution learning (artificial intelligence) matrix algebra sampling methods Cauchy distribution RP matrix estimation-of-distribution algorithm heavy tailed distribution high dimensional EDA high dimensional black-box optimisation multivariate Cauchy sampling multivariate heavy tailed search distribution multivariate modelling random projections ensemble based approach Buildings Covariance matrices Educational institutions Optimization Search problems Sociology estimation of distribution algorithm Multivariate Cauchy EDA Multivariate Gaussian EDA Random Projection Gaussian distribution tabu search European Defence Agency electronic design automation Electronic design automation and methodology EDA gene Educational institutions Sociology matrix algebra sampling methods Buildings Cauchy Allocation algorithm Multivariate DIMENSIONAL

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A new algorithm of automatic programming: GEGEP

引用

6th International Conference on Simulated Evolution and Learning

作者： Du, Xin Li, Yueqiao Xie, Datong Kang, Lishan China Univ Geosci Dept Comp Sci & Technol Wuhan Peoples R China Shijiazhuang Univ Econ Dept Informat & Engn Shijiazhuang Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)3540473319

Gene Expression Programming (GEP) has wide searching ability, simple representation, powerful genetic operators and the creation of high levels of complexity. However, it has some shortcomings, such as blind searching and when dealing with complex problems, its genotype under Karva notation does not allow hierarchical composition of the solution, which impairs the efficiency of the algorithm. So a new automatic programming method is proposed: Gene Estimated Gene Expression Programming(GEGEP) which combines the advantages of estimation of distribution algorithm (EDA) and basic GEP. Compared with basic GEP, it mainly has the following characteristics: First, improve the gene expression structure, the head of gene is divided into a head and a body, which can be used to introduce learning mechanism. Second, the homeotic gene which is also composed of a head, a body and a tail is used which can increase its searching ability. Third, the idea of EDA is introduced, which can enhance its learning ability and accelerate convergence rate. The results of experiments show that GEGEP has better fitting and predicted precision, faster convergence speed than basic GEP and traditional GP.

关键词： Gene Expression Programming genetic programming Gene Estimated Gene Expression Programming estimation of distribution algorithm

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A Comparison of GEC-Based Feature Selection and Weighting for Multimodal Biometric Recognition

A Comparison of GEC-Based Feature Selection and Weighting fo...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Alford, Aniesha Popplewell, Khary Dozier, Gerry Bryant, Kelvin Kelly, John Adams, Josh Abegaz, Tamirat Shelton, Joseph Ricanek, Karl Woodard, Damon L. N Carolina Agr & Tech State Univ Ctr Adv Studies Ident Sci Greensboro NC USA Univ N Carolina Wilmington Ctr Adv Studies Ident Sci Wilmington NC USA Clemson Univ Ctr Adv Studies Ident Sci Clemson SC 29631 USA

ISBN: (纸本)9781424478354

In this paper, we compare the performance of a Steady-State Genetic algorithm (SSGA) and an estimation of distribution algorithm (EDA) for multi-biometric feature selection and weighting. Our results show that when fusing face and periocular modalities, SSGA-based feature weighting (GEFeW(SSGA)) produces higher average recognition accuracies, while EDA-based feature selection (GEFeS(EDA)) performs better at reducing the number of features needed for recognition.

关键词： Eigenface estimation of distribution algorithm Feature Selection Feature Weighting Local Binary Pattern Steady-State Genetic algorithm

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Research Survey and Application on Ground Holding Policy 2

Research Survey and Application on Ground Holding Policy

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IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yan, Yongjie Zhang, Yan Tian, Jing Elect Technol Grp Corp State Key Lab Air Traff Management Syst & Technol Res Inst China 28 Nanjing Peoples R China Elect Technol Grp Corp Res Inst China 28 Nanjing Peoples R China

ISBN: (纸本)9781538692981

This paper introduces the origination, concept of ground holding policy and research literature review firstly. Next, three types of models are built for single airport ground holding problem (SAGHP), multi airport ground holding problem (MAGHP) and dynamic single airport ground holding problem (dSAGHP). For solving algorithms, two conventional methods of First Come First Served (FCFS) and dynamic Sequencing Window are explained briefly, then one heuristic algorithm named estimation of distribution algorithm (EDA) is illustrated in detail to solve the ground delay problems, including problem-specific modeling, probability learning and new solutions sampling. Finally, experiment results based on simulation instances and discussions are given.

关键词： Air Traffic Flow Management Single Airport Ground Holding Problem Multi-objective Optimization estimation of distribution algorithm

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Linkage identification for real-valued loci by fitness difference classification

Linkage identification for real-valued loci by fitness diffe...

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IEEE Congress on Evolutionary Computation

作者： Tsuji, M Munetomo, M Akama, K Hokkaido Univ Sapporo Hokkaido Japan

ISBN: (纸本)0780393635

In order to enhance efficiency of genetic algorithms, it is important to identify a linkage set, i.e. a set of loci tightly linked to construct a building block. In this paper, we propose a novel linkage identification method for real-valued strings called the real-valued Dependency Detection for distribution Derived from df (rD(5)). It can detect linkage sets with quasi-linear fitness evaluations. The rD(5) is designed based on the D-5 which has been proposed for binary strings. It detects dependencies of loci by estimating the distribution of strings classified according to fitness differences. The rD(5) and the LINC-R which is one of linkage identification methods proposed elsewhere, provide approximate equivalent information about a function to be solved, however, the rD(5) performs smaller number of fitness evaluations than the LINC-R for larger functions. Although estimation of distribution algorithms (EDAs) also estimate distribution of strings, it is difficult for EDAs to solve a function composed of exponentially scaled sub-functions. The proposed method, by contrast, can be applied to the function in the similar way to as to a function composed of uniformly scaled sub-functions which is easy for EDAs. We perform experiments to compare the proposed method with the LINC-R and to examine the scaling effect stability of the rD(5). We also investigate two parameters, that define the amount of perturbation (mutation) and that define the quantization level.

关键词： estimation theory genetic algorithms pattern classification string matching binary strings estimation of distribution algorithm exponentially scaled subfunctions fitness difference classification linkage identification mutation perturb Genetic algorithm (GA) binary string estimation theory String matching Pattern recognition Cosmic strings quantization level Allocation algorithm health

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Flexible Learning of k-Dependence Bayesian Network Classifiers 11

Flexible Learning of k-Dependence Bayesian Network Classifie...

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Rubio, Arcadio Gamez, Jose A. European Ctr Soft Comp Collaborat Intelligent Syst Unit C Gonzalo Gutierrez Quiros S-N Mieres 33600 Asturias Spain Univ Castilla La Mancha Comp Syst Dept Albacete 02071 Spain

ISBN: (纸本)9781450305570

In this paper we present an extension to the classical k-dependence Bayesian network classifier algorithm. The original method intends to work for the whole continuum of Bayesian classifiers, from naive Bayes to unrestricted networks. In our experience, it performs well for low values of k. However, the algorithm tends to degrade in more complex spaces, as it greedily tries to add k dependencies to all feature nodes of the resulting net. We try to overcome this limitation by seeking for optimal values of k on a feature per feature basis. At the same time, we look for the best feature ordering. That is, we try to estimate the joint probability distribution of optimal feature orderings and individual number of dependencies. We feel that this preserves the essence of the original algorithm, while providing notable performance improvements.

关键词： Bayesian network classifier estimation of distribution algorithm flexible learning

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SSGA & EDA Based Feature Selection and Weighting for Face Recognition

SSGA & EDA Based Feature Selection and Weighting for Face Re...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Abegaz, Tamirat Dozier, Gerry Bryant, Kelvin Adams, Joshua Shelton, Joseph Ricanek, Karl Woodard, Damon L. N Carolina Agr & Tech State Univ Ctr Adv Studies Ident Sci Greensboro NC 27411 USA

ISBN: (纸本)9781424478354

In this paper, we compare genetic and evolutionary feature selection (GEFeS) and weighting (GEFeW) using a number of biometric datasets. GEFeS and GEFeW have been implemented as instances of Steady-State Genetic and estimation of distribution algorithms. Our results show that GEFeS and GEFeW dramatically improve recognition accuracy as well as reduce the number of features needed for facial recognition. Our results also show that the estimation of distribution algorithm implementation of GEFeW has the best overall performance.

关键词： Face Recognition Steady State Genetic algorithm estimation of distribution algorithm Eigenface Feature Selection

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REMEDA: Random Embedding EDA for Optimising Functions with Intrinsic Dimension

REMEDA: Random Embedding EDA for Optimising Functions with I...

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14th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Sanyang, Momodou L. Kaban, Ata Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ Gambia Sch Informat Technolgy & Commun Brikama CampusPOB 3530 Serekunda Gambia

ISBN: (纸本)9783319458236;9783319458229

It has been observed that in many real-world large scale problems only few variables have a major impact on the function value: While there are many inputs to the function, there are just few degrees of freedom. We refer to such functions as having a low intrinsic dimension. In this paper we devise an estimation of distribution algorithm (EDA) for continuous optimisation that exploits intrinsic dimension without knowing the influential subspace of the input space, or its dimension, by employing the idea of random embedding. While the idea is applicable to any optimiser, EDA is known to be remarkably successful in low dimensional problems but prone to the curse of dimensionality in larger problems because its model building step requires large population sizes. Our method, Random Embedding in estimation of distribution algorithm (REMEDA) remedies this weakness and is able to optimise very large dimensional problems as long as their intrinsic dimension is low.

关键词： estimation of distribution algorithm Black-box optimization Intrinsic dimension

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Evolutionary Approaches to Optimization Problems in Chimera Topologies 16

Evolutionary Approaches to Optimization Problems in Chimera ...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Santana, Roberto Zhu, Zheng Katzgraber, Helmut Univ Basque Country UPV EHU San Sebastian Spain Texas A&M Univ College Stn TX USA

ISBN: (纸本)9781450342063

Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over 1000 hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.

关键词： quantum computing Ising model estimation of distribution algorithm genetic algorithms instance analysis

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Analysing a Hybrid Model-Based Evolutionary algorithm for a Hard Grouping Problem 16th

Analysing a Hybrid Model-Based Evolutionary Algorithm for a ...

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16th International Conference on Computer Aided Systems Theory (EUROCAST)

作者： Raggl, Sebastian Beham, Andreas Wagner, Stefan Affenzeller, Michael Univ Appl Sci Upper Austria Heurist & Evolutionary Algorithms Lab Softwarepk 11 A-4232 Hagenberg Austria

ISBN: (纸本)9783319747187;9783319747170

We present a new hybrid model-based algorithm called Memetic Path Relinking (MemPR). MemPR incorporates ideas of memetic, evolutionary, model-based algorithms and path relinking. It uses different operators that compete to fill a small population of high quality solutions. We present a new hard grouping problem derived from a real world transport lot building problem. In order to better understand the algorithm as well as the problem we analyse the impact of the different operators on solution quality and which operators perform best at which stage of optimisation. Finally we compare MemPR to other state-of-the-art algorithms and find that MemPR outperforms them on real-world problem instances.

关键词： Hybrid algorithm Memetic algorithm estimation of distribution algorithm Grouping problem

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