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检索条件"主题词=Estimation of distribution algorithms"

共 309 条记录，以下是231-240 订阅

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An Implicitly Parallel EDA Based on Restricted Boltzmann Machines 14

An Implicitly Parallel EDA Based on Restricted Boltzmann Mac...

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Probst, Melte Rothlauf, Franz Grahl, Joern Johannes Gutenberg Univ Mainz Dept Informat Syst & Business Adm D-55122 Mainz Germany

ISBN: (纸本)9781450326629

We present a parallel version of RBM-EDA. RBM-EDA is an estimation of distribution Algorithm (FDA) that models dependencies between decision variables using a Restricted Boltzmann Machine (RBM). In contrast to other EDAs, RBM-EDA mainly uses matrix-matrix ultiplications for model estimation and sampling. Hence, for implementation, standard libraries for linear algebra can be used. This allows an easy parallelization and leads to a high utilization of parallel architectures. The probabilistic model of the parallel version and the version on a single core are identical. We explore the speedups gained from running RBM-EDA on a Graphics Processing Unit. For problems of bounded difficulty like deceptive traps, parallel RBM-EDA is faster by several orders of magnitude (up to 750 times) in comparison to a single-threaded implementation on a CPU. As the speedup grows linearly with problem size, parallel RBM-EDA may be particularly useful for large problems.

关键词： Parallelization Restricted Boltzmann Machines estimation of distribution algorithms Machine Learning Combinatorial Optimization Problems GPU Neural Networks

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Gaussian Mixture Modeling for Dynamic Particle Swarm Optimization of Recurrent Problems 12

Gaussian Mixture Modeling for Dynamic Particle Swarm Optimiz...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Vellasques, Eduardo Sabourin, Robert Granger, Eric Univ Quebec Ecole Technol Super Montreal PQ H3C 3P8 Canada

ISBN: (纸本)9781450311779

In dynamic optimization problems, the optima location and fitness value change over time. Techniques in literature for dynamic optimization involve tracking one or more peaks moving in a sequential manner through the parameter space. However, many practical applications in, e. g., video and image processing involve optimizing a stream of recurrent problems, subject to noise. In such cases, rather than tracking one or more moving peaks, the focus is on managing a memory of solutions along with information allowing to associate these solutions with their respective problem instances. In this paper, Gaussian Mixture Modeling (GMM) of Dynamic Particle Swarm Optimization (DPSO) solutions is proposed for fast optimization of streams of recurrent problems. In order to avoid costly re-optimizations over time, a compact density representation of previously-found DPSO solutions is created through mixture modeling in the optimization space, and stored in memory. For proof of concept simulation, the proposed hybrid GMM-DPSO technique is employed to optimize embedding parameters of a bi-tonal watermarking system on a heterogeneous database of document images. Results indicate that the computational burden of this watermarking problem is reduced by up to 90.4% with negligible impact on accuracy.

关键词： Particle Swarm Optimization Dynamic Optimization Gaussian Mixture Models estimation of distribution algorithms

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estimation of Fitness Landscape Contours in EAs 07

Estimation of Fitness Landscape Contours in EAs

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Posik, Petr Franc, Vojtech Czech Tech Univ Dept Cybernet Tech 2 Prague 16627 6 Czech Republic Fraunhofer FIRST IDA D-12489 Berlin Germany

ISBN: (纸本)9781595936974

Evolutionary algorithms applied in real domain should profit from information about the local fitness function curvature. This paper presents an initial study of art evolutionary strategy with a novel approach for learning the covariance matrix of a Gaussian distribution. The learning method is based on estimation of the fitness landscape contour line between the selected and discarded individuals. The distribution learned this way, is then used to generate new population members. The algorithm presented here is the first attempt to construct the Gaussian distribution this way and should be considered only a proof of concept;nevertheless, the empirical comparison on low-dimensional quadratic functions shows that our approach is viable and with respect to the number of evaluations needed to find a solution of certain quality, it is comparable to the state-of-the-art CMA-ES in case of sphere function and outperforms the CMA-ES in case of elliptical function.

关键词： evolutionary computation estimation of distribution algorithms learnable evolution model function optimization separating ellipsoid

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The HGEDA Hybrid Algorithm For OLAP Data Cubes

The HGEDA Hybrid Algorithm For OLAP Data Cubes

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3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Jia, Chen Chen, HongWei Wuhan Textile Univ Sch Comp Sci & Technol Wuhan Peoples R China Wuhan Tech Coll Commun Dept Transportat Engn Wuhan Peoples R China

ISBN: (纸本)9783037852866

On-Line Analytical Processing (OLAP) tools are frequently used in business, science and health to extract useful knowledge from massive databases. An important and hard optimization problem in OLAP data warehouses is the view selection problem, consisting of selecting a set of aggregate views of the data for speeding up future query processing. In this paper we present a new approach, named HGEDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. Experimental results show that the HGEDA are competitive with the genetic algorithm on a variety of problem instances, often finding approximate optimal solutions in a reasonable amount of time.

关键词： query optimization estimation of distribution algorithms data warehousing OLAP view selection

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An Adaptive Niching EDA Based on Clustering Analysis

An Adaptive Niching EDA Based on Clustering Analysis

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2010 IEEE World Congress on Computational Intelligence

作者： Chen, Benhui Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9781424481262

estimation of distribution algorithms (EDAs) still suffer from the drawback of premature convergence for solving the optimization problems with irregular and complex multimodal landscapes. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first is the continuous EDA based on single Gaussian probabilistic model to solve two benchmark functional multimodal optimization problems. The second is a real complicated discrete EDA optimization problem, the protein 3-D HP model based on k-order Markov probabilistic model. The experiment studies demonstrate that the proposed adaptive niching EDA is an efficient method.

关键词： Markov processes adaptive niching EDA affinity propagation clustering analysis balance niching searching strategy biology computing complex multimodal landscapes convergence data mining estimation of distribution algorithms functional multimodal optimization problems irregular multimodal landscapes k-order Markov probabilistic model optimisation pattern clustering premature convergence protein 3D HP model query formulation searching information mining single Gaussian probabilistic model

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EDA-Based Logistic Regression Applied to Biomarkers Selection in Breast Cancer

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Gonzalez, S. Robles, V. Pena, J. M. Cubo, O. Univ Politecn Madrid Dept Comp Architecture Madrid Spain

ISBN: (纸本)9783642024801

Logistic regression (LR) is a simple and efficient;supervised learning algorithm for estimating the probability of an outcome variable. This algorithm is widely accepted and used in medicine for classification of diseases using DNA microarray data. Classical LR. does not perform well for microarrays when applied directly, because the number of variables exceeds the number of samples. However, by reducing the number of genes and selecting specific variables (using filtering methods) great results can be obtained with this algorithm. On this contribution we propose a novel approach for fitting the (penalized) LR. models based on EDAs. Breast Cancer dataset has been proposed to compare both accuracy and gene selection.

关键词： Breast Cancer Biomarker Feature Selection Logistic Regression Evolutionary algorithms estimation of distribution algorithms

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Denoising Autoencoder Genetic Programming for Real-World Symbolic Regression 22

Denoising Autoencoder Genetic Programming for Real-World Sym...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Wittenberg, David Rothlauf, Franz Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9781450392686

Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural-network based estimation of distribution genetic programming algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of genetic programming (GP). Recent work demonstrated that the DAE-GP outperforms standard GP. However, results are limited to the generalization of the royal tree problem. In this work, we apply the DAE-GP to real-world symbolic regression. On the Airfoil dataset and given a fixed number of fitness evaluations, we find that the DAE-GP generates significantly better and smaller (number of nodes) best candidate solutions than standard GP. The results highlight that the DAE-GP may be a good alternative for generating good and interpretable solutions for real-world symbolic regression.

关键词： Genetic Programming estimation of distribution algorithms Denoising Autoencoders Symbolic Regression

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Independence-based MAP for Markov networks structure discovery

Independence-based MAP for Markov networks structure discove...

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23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Bromberg, Facundo Schlueter, Federico Edera, Alejandro Univ Tecnol Nacl Fac Reg Mendoza Dept Sistemas Informac Mendoza Argentina

ISBN: (纸本)9780769545967

This work presents IBMAP, an approach for robust learning of Markov network structures from data;together with IBMAP-HC, an efficient instantiation of the approach. Existing Score-Based (SB) and Independence-Based (IB) approaches must make concessions either on robustness or efficiency. IBMAP-HC improves robustness efficiently through an IB-SB hybrid approach based on the probabilistic Maximum-A-Posteriori (MAP) technique;and the IB-score, a tractable expression for computing posterior probabilities of Markov network structures. Performance is first tested against IB and SB competitors on synthetic datasets. Against IB competitors (GSMN algorithm and a version of the HHC algorithm adapted here for Markov networks discovery), IBMAP-HC showed reductions in edges Hamming distance with same order running times. Against SB competitors, both IBMAP-HC and our adaptation of HHC produced comparable Hamming distances, but with running times orders of magnitude faster. We also evaluated IBMAP-HC in a realistic, challenging testbed: EDAs, evolutionary algorithms for optimization that estimate a distribution on each generation. Using IBMAP-HC to estimate distributions, EDAs converged to the optimum faster in all benchmark functions considered, reducing required fitness evaluations by up to 80%.

关键词： Graphical Models Markov networks Structure Learning estimation of distribution algorithms

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Evaluation relaxation using substructural information and linear estimation 06

Evaluation relaxation using substructural information and li...

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8th Annual Genetic and Evolutionary Computation Conference

作者： Sastry, Kumara Lima, Claudio F. Goldberg, David E. Univ Illinois Dept Ind & Enterprise Syst Engn Illinois Genet Algorithms Lab IlliGAL Urbana IL 61801 USA Univ Algarve DEEI FCT P-8000117 Faro Portugal

ISBN: (纸本)9781595931863

The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that A surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75-3.1) on a class of boundedly-difficult additively-decomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noise-to-signal ratio.

关键词： estimation of distribution algorithms extended compact genetic algorithms fitness surrogates efficiency enhancement evaluation relaxation linear regression speed-up

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Research on the Optimization Model for Target Assignment of Multiple UAVs Based on Probability Equilibrium Strategy

Research on the Optimization Model for Target Assignment of ...

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International Conference on Industrial Control and Electronics Engineering (ICICEE)

作者： Zhang Yi Liu Aili Yang Xiuxia Department of Control Engineering Naval Aeronautical and Astronautical University

ISBN: (纸本)9780769547923

A new algorithm based on probability collectives is proposed to solve the target assignment of multiple unmanned aerial vehicles (UAVs) to coordinated attack multi-target without centric control, while keeping the probability equilibrium of every UAV weapons. The estimation of distribution algorithms is used to solving the Multiple UAVs target assignment, which is proper to solve the discrete combination optimization. Simulation results show the accuracy of the optimization model for target assignment. Compared with conventional methods, the proposed method is able to converge to the global optimum with more stability, scalability and faster convergence rate.

关键词： Multiple UAVs target assignment probability equilibrium estimation of distribution algorithms

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