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

共 309 条记录，以下是261-270 订阅

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Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems

Unbalanced Mallows Models for Optimizing Expensive Black-Box...

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2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Irurozki, Ekhine Lopez-Ibanez, Manuel Telecom Paris Paris France Univ Malaga Malaga Spain

ISBN: (纸本)9781450383509

Expensive black-box combinatorial optimization problems arise in practice when the objective function is evaluated by means of a simulator or a real-world experiment. Since each fitness evaluation is expensive in terms of time or resources, the number of possible evaluations is typically several orders of magnitude smaller than in non-expensive problems. Classical optimization methods are not useful in this scenario. In this paper, we propose and analyze UMM, an estimation-of-distribution (EDA) algorithm based on a Mallows probabilistic model and unbalanced rank aggregation (uBorda). Experimental results on black-box versions of LOP and PFSP show that UMM outperforms the solutions obtained by CEGO, a Bayesian optimization algorithm for combinatorial optimization. Nevertheless, a slight modification to CEGO, based on the different interpretations for rankings and orderings, significantly improves its performance, thus producing solutions that are slightly better than those of UMM and dramatically better than the original version. Another benefit of UMM is that its computational complexity increases linearly with both the number of function evaluations and the permutation size, which results in computation times an order of magnitude shorter than CEGO, making it specially useful when both computation time and number of evaluations are limited.

关键词： Combinatorial optimization Bayesian optimization Expensive blackbox optimization estimation of distribution algorithms

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Entropy-Based Evaluation Relaxation Strategy for Bayesian Optimization Algorithm

Entropy-Based Evaluation Relaxation Strategy for Bayesian Op...

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23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Luong, Hoang Ngoc Nguyen, Hai Thanh Thi Ahn, Chang Wook Sungkyunkwan Univ Sch Informat & Commun Engn Suwon 440746 South Korea

ISBN: (纸本)9783642130243

Bayesian Optimization Algorithm (BOA) belongs to the advanced evolutionary algorithms (EA) capable of solving problems with multivariate interactions However, to attain wide applicability in real-world world optimization, BOA needs to be coupled with various efficiency enhancement techinques A BOA incorporated with a novel entropy-based (cBOA) is developed in this regard Composed of an on-demand evaluation strategy (ODES) and a sporadic evaluation method eBOA significantly reduces the number of (fitness) evaluations without, imposing any larger population-sizing requirement experiments adduce the grounds for its significant improvement, in the number of evaluations until reliable convergence Furthermore;the evaluation relaxation does not negatively affect the scalability performance

关键词： estimation of distribution algorithms ayesian Optimization Algorithm Evaluation Relaxation On-Demand Evaluation

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Mixtures of Kikuchi approximations

Mixtures of Kikuchi approximations

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17th European Conference on Machine Learning (ECML 2006)

作者： Santana, Roberto Larranaga, Pedro Lozano, Jose A. Univ Basque Country Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence E-20080 San Sebastian Spain

ISBN: (纸本)354045375X

Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.

关键词： mixture of distributions Kikuchi approximations estimation of distribution algorithms EM

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Bayesian Immigrant Diploid Genetic Algorithm for Dynamic Environments 1

引用

14th Biennial International Conference on Artificial Evolution (EA)

作者： Gazioglu, Emrullah Etaner-Uyar, A. Sima Istanbul Tech Univ Istanbul Turkey

ISBN: (数字)9783030457150

ISBN: (纸本)9783030457150

In dynamic environments, the main aim of an optimization algorithm is to track the changes and to adapt the search process. In this paper, we propose an approach called the Bayesian Immigrant Diploid Genetic Algorithm (BIDGA). BIDGA uses implicit memory in the form of diploid chromosomes, combined with the Bayesian Optimization Algorithm (BOA), which is a form of estimation of distribution algorithms (EDAs). Through the use of BOA, BIDGA is able to take into account epistasis in the form of binary relationships between the variables. Experiments show that the proposed approach is efficient and also indicates that exploiting interactions between variables is important to adapt to the newly formed environments.

关键词： Evolutionary algorithms estimation of distribution algorithms Bayesian Optimization Algorithm Dynamic environments

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Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search 1

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25th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar Conference

作者： Wittenberg, David Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (数字)9783031020568

ISBN: (纸本)9783031020568;9783031020551

Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming (EDA-GP) algorithm that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). At each generation, the idea is to flexibly identify promising properties of the parent population and to transfer these properties to the offspring where the DAE-GP uses denoising to make the model robust to noise that is present in the parent population. Denoising partially corrupts candidate solutions that are used as input to the model. The stronger the corruption, the stronger the generalization of the model. In this work, we study how corruption strength affects the exploration and exploitation behavior of the DAE-GP. For a generalization of the royal tree problem (high-locality problem), we find that the stronger the corruption, the stronger the exploration of the solution space. For the given problem, weak corruption resulting in a stronger exploitation of the solution space performs best. However, in more rugged fitness landscapes (low-locality problems), we expect that a stronger corruption resulting in a stronger exploration will be helpful. Choosing the right denoising strategy can therefore help to control the exploration and exploitation behavior in search, leading to an improved search quality.

关键词： Genetic Programming estimation of distribution algorithms Probabilistic Model-Building Denoising Autoencoders

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A Novel Evolutionary Morphological Descriptor for the Segmentation of Coronary Arteries

A Novel Evolutionary Morphological Descriptor for the Segmen...

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5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Cruz-Aceves, Ivan Ivvan-Valdez, S. Ctr Invest Matemat CIMAT AC CONACYT Jalisco S-N Guadalajara 36000 Jalisco Mexico Univ Guanajuato Div Ingn DICIS Salamanca 36885 Mexico

ISBN: (纸本)9781538646267

This paper presents a novel evolutionary morphological descriptor for the detection of coronary arteries in X-ray angiograms. The method consists of the vessel detection and segmentation steps. In the first step, the univariate marginal distribution algorithm (UMDA) is used to design the optimal binary template for detecting vessel-like structures by applying the morphological top-hat operator in the spatial image domain. The results of the evolutionary descriptor are compared with those obtained by using methods based on the Hessian matrix, Gaussian filters and top-hat operator. The proposed method obtained the highest performance in terms of the area (A(z)) under the ROC curve (A(z) = 0.9661) compared with five stateof- the-art vessel detection methods using a training set of 50 angiograms and A(z) = 0.9532 with an independent test set of 50 angiograms. In the second step, six thresholding methods are compared in terms of segmentation accuracy in order to classify vessel and nonvessel pixels from the evolutionary filter response. Finally, the experimental results for vessel segmentation with the proposed method provided an accuracy of 0.9580 with the test set of angiograms.

关键词： Automatic segmentation Coronary angiograms estimation of distribution algorithms Top-hat operator Vessel enhancement

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Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin Glasses 07

Analyzing Probabilistic Models in Hierarchical BOA on Traps ...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Hauschild, Mark Pelikan, Martin Lima, Claudio F. Sastry, Kumara Univ Missouri MEDAL Dept Math & Comp Sci 320 CCBOne Univ Blvd St Louis MO 63121 USA Univ Algarve Informat Lab UALG ILAB Dept Elect & Comp Sci Engn P-8000117 Faro Portugal Univ Illinois Dept Ind & Enterprise Syst Engn Illinois Genet Algorithms Lab ILLiGAL Urbana IL 61801 USA

ISBN: (纸本)9781595936974

The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.

关键词： Bayesian optimization algorithm hierarchical BOA estimation of distribution algorithms model accuracy

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Analysis of Epistasis Correlation on NK Landscapes with Nearest-Neighbor Interactions 11

Analysis of Epistasis Correlation on NK Landscapes with Near...

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Pelikan, Martin Univ Missouri Dept Math & Comp Sci Missouri Estimat Distribut Algorithms Lab St Louis MO 63121 USA

ISBN: (纸本)9781450305570

Epistasis correlation is a measure that estimates the strength of interactions between problem variables. This paper presents an empirical study of epistasis correlation on a large number of random problem instances of NK landscapes with nearest neighbor interactions. The results are analyzed with respect to the performance of hybrid variants of two evolutionary algorithms: (1) the genetic algorithm with uniform crossover and (2) the hierarchical Bayesian optimization algorithm.

关键词： Epistasis epistasis correlation problem difficulty NK landscapes genetic algorithms estimation of distribution algorithms linkage learning

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Application of Node Based Coincidence Algorithm for Flow Shop Scheduling Problems

Application of Node Based Coincidence Algorithm for Flow Sho...

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10th International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Srimongkolkul, Ornrumpha Chongstitvatana, Prabhas Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781479908066;9781479908059

This work proposes an algorithm called Node Based Coincidence algorithm (NB-COIN) focusing on total flowtime minimization in the permutation flowshop scheduling problems. Many algorithms have been proved to be effective for this problem. However, in the real situation, cost of computation becomes an important factor. NB-COIN produces reasonable solutions using a lot less computation power than other algorithms in consideration. Compared to a number of well-known algorithms, the results show that NB-COIN is an effective algorithm which generates less than 1.7% different from recently best known solutions from Taillard's benchmark instances.

关键词： Coincidence Algorithm permutation flowshop scheduling estimation of distribution algorithms

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HYBRID MUSIC RECOMMENDER USING CONTENT-BASED AND SOCIAL INFORMATION 41

HYBRID MUSIC RECOMMENDER USING CONTENT-BASED AND SOCIAL INFO...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chiliguano, Paulo Fazekas, Gyorgy Queen Mary Univ London Sch Elect Engn & Comp Sci Mile End Rd London E1 4NS England

ISBN: (纸本)9781479999880

Internet resources available today, including songs, albums, playlists or podcasts, that a user cannot discover if there is not a tool to filter the items that the user might consider relevant. Several recommendation techniques have been developed since the Internet explosion to achieve this filtering task. In an attempt to recommend relevant songs to users, we propose an hybrid recommender that considers real-world users information and high-level representation for audio data. We use a deep learning technique, convolutional deep neural networks, to represent an audio segment in a n-dimensional vector, whose dimensions define the probability of the segment to belong to a specific music genre. To capture the listening behavior of a user, we investigate a state-of-the-art technique, estimation of distribution algorithms. The designed hybrid music recommender outperforms the predictions compared with a traditional content-based recommender.

关键词： Deep Learning Convolutional Neural Networks estimation of distribution algorithms Recommender Systems MIR

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