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检索条件"主题词=Estimation Of Distribution Algorithm"

共 435 条记录，以下是401-410 订阅

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The N-Tuple Bandit Evolutionary algorithm for Game Agent Optimisation

The N-Tuple Bandit Evolutionary Algorithm for Game Agent Opt...

引用

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

作者： Lucas, Simon M. Liu, Jialin Perez-Liebana, Diego Queen Mary Univ London Sch Elect Engn & Comp Sci London England

ISBN: (纸本)9781509060177

This paper describes the N-Tuple Bandit Evolutionary algorithm (NTBEA), an optimisation algorithm developed for noisy and expensive discrete (combinatorial) optimisation problems. The algorithm is applied to two game-based hyperparameter optimisation problems. The N-Tuple system directly models the statistics, approximating the fitness and number of evaluations of each modelled combination of parameters. The model is simple, efficient and informative. Results show that the NTBEA significantly outperforms grid search and an estimation of distribution algorithm.

关键词： estimation of distribution algorithm Evolutionary algorithm Hyper-Parameter Optimisation Rolling Horizon Evolution Game Playing Agent Noisy Optimisation

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Multivariate Cauchy EDA Optimisation

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15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

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

ISBN: (纸本)9783319108407;9783319108391

We consider Black-Box continuous optimization by estimation of distribution algorithms (EDA). In continuous EDA, the multivariate Gaussian distribution is widely used as a search operator, and it has the well-known advantage of modelling the correlation structure of the search variables, which univariate EDA lacks. However, the Gaussian distribution as a search operator is prone to premature convergence when the population is far from the optimum. Recent work suggests that replacing the univariate Gaussian with a univariate Cauchy distribution in EDA holds promise in alleviating this problem because it is able to make larger jumps in the search space due to the Cauchy distribution's heavy tails. In this paper, we propose the use of a multivariate Cauchy distribution to blend together the advantages of multivariate modelling with the ability of escaping early convergence to efficiently explore the search space. Experiments on 16 benchmark functions demonstrate the superiority of multivariate Cauchy EDA against univariate Cauchy EDA, and its advantages against multivariate Gaussian EDA when the population lies far from the optimum.

关键词： Multivariate Gaussian distribution Multivariate Cauchy distribution estimation of distribution algorithm Black-box Optimization

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Discovering gene-gene interactions by inferring dags of Bayesian networks with EDA

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11th Joint International Computer Conference (JICC 2005)

作者： Dai, C Liu, J Wuhan Univ Sch Comp Wuhan 430079 Peoples R China

ISBN: (纸本)9812565329

More and more experimental results have shown that genes do not function independently;instead they act on each other. To find the interactions among genes is one of the hottest topics in current genome research. The Bayesian Network (BN), which is a graph-based representation of a joint probability distribution that captures properties of conditional independence between variables, is a desirable tool to find the interaction between genes. However, how to find appropriate BNs that most fit to the data is very difficult since the number of possible BNs on n variables is the super-exponential of n. To avert the combinational explosion, in this paper, we use estimation of distribution algorithm (EDA) to search the BN space. Also, in order to make the individuals of EDA meaningful, we also propose a depth-first search method to cut circles in the graph. We have tested our method on cell-cycle gene expression data, the results show that the constructed BNs can not only discover some existing relationships in other literatures and Gene Ontology, but also reveal some previously unknown interactions.

关键词： Bayesian network gene-gene interaction estimation of distribution algorithm

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Efficient Multiple Lines Outage Detection in SmartGrid 2

Efficient Multiple Lines Outage Detection in SmartGrid

引用

Power Generation System and Renewable Energy Technologies (PGSRET)

作者： Ahmed, Ashfaq Awais, Muhammad Naeem, Muhammad Iqbal, Muhammad Alpalagan, Alagan COMSATS Inst Informat Technol Wah Campus Islamabad Pakistan Ryerson Univ Toronto ON Canada

ISBN: (纸本)9781467368131

To avoid blackouts in the energy system, the knowledge of the state of the power lines has critical importance. To get the timely conscience, Phasor Measurement Units (PMUs) are used to provide real-time synchronized measurements of voltage and current phasors of the buses. The deployment of PMUs is done to first detect any single or multiple line outage, and further to identify the correct line in outage. But if these approaches are applied to more multiple line outages, the search space grows exponentially. To reduce the computational complexity in case of multiple line outage detection, stochastic optimization methods can be used. These methods give near optimal solution with an acceptable computational complexity. In this paper, the use of phasor angle measurements and optimal deployment of PMUs to identify multiple power line outages is investigated. An adaptive version of estimation of distribution algorithm (EDA) is proposed to detect and identify the lines in outage. In this adaptive EDA thresholding technique is introduced in order to get better solutions. It is shown that the proposed algorithm is achieving a better success rate than the other evolutionary techniques.

关键词： estimation of distribution algorithm smart grids power systems line outages

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Coordinated Generation and Transmission Planning Based on EDA/DA algorithm Considering Wind Power Integration 7

Coordinated Generation and Transmission Planning Based on ED...

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7th IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

作者： Dong, Jinxin Han, Hui Ren, Xiusheng Wang, Zheng Wang, Jiawei Zhao, Haibo SGCC SEPC Econ & Tech Res Inst Taiyuan Shanxi Peoples R China North China Elect Power Univ Beijing Peoples R China

ISBN: (纸本)9781538649503

The large-scale transmission network expansion planning requires high computational speed and accuracy, so combining the DE (differential evolution) algorithm with the EDA (estimation of distribution algorithm), whose algorithm probability update mechanism is improved according to the characteristics of the transmission network expansion problem, a EDA/DE hybrid algorithm is presented to solve large-scale transmission network expansion planning problem. At the same time, taking into account the grid company investments, incentive policy for renewable energy as well as the security constraints, based on the embedded cost, wind curtailment and risk value, a multi-objective static planning model for transmission network expansion and wind power network optimization is established. IEEE24 RTS node example demonstrates the validity and applicability of the proposed algorithm.

关键词： Differential evolution estimation of distribution algorithm transmission planning wind power risk

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Probabilistic Based Evolutionary Optimizers in Bi-objective Travelling Salesman Problem

Probabilistic Based Evolutionary Optimizers in Bi-objective ...

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8th International Conference of Simulated Evolution and Learning

作者： Shim, Vui Ann Tan, Kay Chen Chia, Jun Yong Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

ISBN: (纸本)9783642172977

This paper studies the probabilistic based evolutionary algorithms in dealing with bi-objective travelling salesman problem. Multi-objective restricted Boltzmann machine and univariate marginal distribution algorithm in binary representation are modified into permutation based representation. Each city is represented by an integer number and the probability distributions of the cities are constructed by running the modeling approach. A refinement operator and a local exploitation operator are proposed in this work. The probabilistic based evolutionary optimizers are subsequently combined with genetic based evolutionary optimizer to complement the limitations of both algorithms.

关键词： estimation of distribution algorithm evolutionary multi-objective optimization restricted Boltzmann machine travelling salesman problem

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Asynchronous Strategy of Parallel Hybrid Approach of GA and EDA for Function Optimization

Asynchronous Strategy of Parallel Hybrid Approach of GA and ...

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3rd International Conference on Networking and Computing (ICNC)

作者： Said, Said Mohamed Nakamura, Morikazu Univ Ryukyus Dept Informat Engn Nishihara Okinawa 9030213 Japan

ISBN: (纸本)9780769548937;9781467346245

This paper adapts parallel master-slave estimation of distribution and genetic algorithms (GAs and EDAs) hybridization. The master selects portions of the search space, and slaves perform, in parallel and independently, a GA that solves the problem on the assigned portion of the search space. The master's work is to progressively narrow the areas explored by the slave's GAs, using parallel dynamic K-means clustering to determine the basins of attraction of the search space. Coordination of activities between master and slaves is done in an asynchronous way (i.e. no waiting is entertained among the processes). The proposed asynchronous model has managed to reduce computation time while maintaining the quality of solutions.

关键词： Hybrid estimation of distribution algorithm Genetic algorithms Synchronous Asynchronous Parallel processing Master-Slave K-means clustering

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Genetic & Evolutionary Biometrics: Feature Extraction from a Machine Learning Perspective

Genetic & Evolutionary Biometrics: Feature Extraction from a...

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IEEE SoutheastCon

作者： Shelton, Joseph Alford, Aniesha Small, Lasanio Leflore, Derrick Williams, Jared Adams, Joshua Dozier, Gerry Bryant, Kelvin Abegaz, Tamirat Ricanek, Karl NC A&T Ctr Adv Studies Ident Sci Greensboro NC USA Clemson Univ Comp Sci Clemson SC USA Univ N Carolina Dept Comp Sci Wilmington NC USA

ISBN: (纸本)9781467313759

Genetic & Evolutionary Biometrics (GEB) is a newly emerging area of study devoted to the design, analysis, and application of genetic and evolutionary computing to the field of biometrics. In this paper, we present a GEB application called GEFEML (Genetic and Evolutionary Feature Extraction Machine Learning). GEFEML incorporates a machine learning technique, referred to as cross validation, in an effort to evolve a population of local binary pattern feature extractors (FEs) that generalize well to unseen subjects. GEFEML was trained on a dataset taken from the FRGC database and generalized well on two test sets of unseen subjects taken from the FRGC and MORPH databases. GEFEML evolved FEs that used fewer patches, had comparable accuracy, and were 54% less expensive in terms of computational complexity.

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

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Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance 19

Approaching the Quadratic Assignment Problem with Kernels of...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Arza, Etor Ceberio, Josu Perez, Aritz Irurozki, Ekhine BCAM Bilbao Spain Univ Basque Country UPV EHU Donostia San Sebastian Spain

ISBN: (纸本)9781450367486

The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size n > 40 are seldom solved using exact methods. In this sense, many approximate methods have been published to tackle this problem, including estimation of distribution algorithms (EDAs). In particular, EDAs have been used to solve permutation problems by introducing distance based exponential models, such as the Mallows Models. In this paper we approximate the QAP with a Hamming distance based kernels of Mallows Models. Based on the benchmark instances, we have observed that our approach is competitive, reaching the best-known solution in 71% of the tested instances, especially on large instances (n > 125), where it is able to outperform state of the art results in 43 out of 288 instances.

关键词： estimation of distribution algorithm Quadratic Assignment Problem Evolutionary algorithm Hamming distance

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Master-Slave Asynchronous Evolutionary Hybrid algorithm and its Application in VANETs Routing Optimization 3

Master-Slave Asynchronous Evolutionary Hybrid Algorithm and ...

引用

3rd IIAI International Conference on Advanced Applied Informatics (IIAI-AAI)

作者： Said, Said Mohamed Nakamura, Morikazu Univ Ryukyus Dept Informat Engn Nishihara Okinawa 9030213 Japan

ISBN: (纸本)9781479941735

Hybrid algorithms incorporated with parallel processing techniques are very powerful tools for efficiently solving very complex optimization problems. We present asynchronous parallel computer architecture adaptation based on hybridization of Genetic algorithms (GAs) and estimation of distribution algorithms (EDAs). In this master-slave formulation, slaves perform evolutionary computation independently using GAs, while master supervises and controls the searching process. Master's role is to probabilistically study the characteristics of solution space and directs the slaves on good searching spots. This study reports some few findings on the ability of our hybrid algorithm to solve some instances of BQP problem as well as AODV routing optimization in VANETs. For both problems our hybrid algorithm has obtained best results in terms of quality of solutions as well as computational speed.

关键词： Hybrid algorithms estimation of distribution algorithm Genetic algorithms Synchronous algorithms Asynchronous algorithms Parallel processing Master-Slave K-means clustering BQP VANETs AODV NS-2

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