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

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Advanced Neighborhoods and Problem Difficulty Measures 11

Advanced Neighborhoods and Problem Difficulty Measures

引用

13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Hauschild, Mark Pelikan, Martin Univ Missouri Dept Math & Comp Sci MEDAL St Louis MO 63121 USA

ISBN: (纸本)9781450305570

While different measures of problem difficulty of fitness landscapes have been proposed, recent studies have shown that many of the common ones do not closely correspond to the actual difficulty of problems when solved by evolutionary algorithms. One of the reasons for this is that most problem difficulty measures are based on neighborhood structures that are quite different from those used in most evolutionary algorithms. This paper examines several ways to increase the accuracy of problem difficulty measures by including linkage information in the measure to more accurately take into account the advanced neighborhoods explored by some evolutionary algorithms. The effects of these modifications of problem difficulty are examined in the context of several simple and advanced evolutionary algorithms. The results are then discussed and promising areas for future research are proposed.

关键词： Genetic algorithms Hierarchical BOA estimation of distribution algorithms difficulty measures

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Research On Traveling Salesman Problem Algorithm

Research On Traveling Salesman Problem Algorithm

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Yun, Xiaoyan Univ Sci & Technol Liaoning Coll Software Engn Anshan 114051 Peoples R China

ISBN: (纸本)9783037856932

The traveling salesman problem (TSP) has been an important problem in the field of distribution and logistics and it is clearly NP-hard combinatorial optimization problem and difficult to solve. This paper gives a review of achievements of different types of algorithms for the traveling sales man problem and outlines these advantages and limitation for these algorithms, including dynamic program, brand and bound, genetic algorithm and estimation of distribution algorithms. In addition, some of the most powerful efficiency enhancement techniques applied to TSP is discussed and quite a few common conditions of different methods for TSP are summarized. Finally, some future research direction and content are proposed.

关键词： Traveling salesman problem dynamic program brand and bound genetic algorithm estimation of distribution algorithms

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An Improved DBOA Based on estimation of Model Similarity 7th

An Improved DBOA Based on Estimation of Model Similarity

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7th International Symposium on Intelligence Computation and Applications (ISICA)

作者： Kong, Yuyan Yao, Jintao Yang, Lei Guangzhou Coll Commerce Guangzhou 511363 Guangdong Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China

ISBN: (纸本)9789811003561;9789811003554

In DBOA, to build accurately the best Bayesian network with respect to most metrics is NP-complete and the high time complexity of learning the model structure becomes a bottleneck of DBOA for real application. Consequently, in order to decrease the asymptotic time complexity of model building and make the algorithm more practical even for extremely large and complex problem, this paper presents adaptive sporadic model building based on estimation of model similarity as an efficiency enhancement technique of DBOA. The results show that performing the adaptive model building in DBOA can reduce the number of building model under no increasing on the number of generation and population size necessary to converge to optimal solutions, and achieve a better trade-off between the convergence speed and convergence results.

关键词： estimation of distribution algorithms Bayesian optimization algorithm Bayesian networks Model similarity Adaptive model building

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Genetic algorithm based on independent component analysis for global optimization

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9th International Conference on Parallel Problem Solving from Nature (PPSN IX)

作者： Li, Gang Lee, Kin Hong Leung, Kwong Sak Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)3540389903

We design a new Genetic Algorithm based on Independent Component Analysis for unconstrained global optimization of continuous function. We use Independent Component Analysis to linearly transform the original dimensions of the problem into new components which are independent from each other with respect to the fitness. We project the population on the independent components and obtain corresponding sub-populations. We apply genetic operators on the sub-populations to generate new sub-populations, and combine them as a new population. In other words, we use Genetic Algorithm to find the optima on the independent components, and combine the optima as the global optimum for the problem. As we actually reduce the original high-dimensional problem into subproblems of much fewer dimensions, the solution space decreases exponentially and thus the problem becomes easier for Genetic Algorithm to solve. The experiment results verified that our algorithm produced optimal or close-to-optimal solutions better than or comparable to those produced by some of other Genetic algorithms and it required much less fitness evaluations of individuals.

关键词： genetic algorithm independent component analysis estimation of distribution algorithms global optimization

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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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Continuous Population-Based Incremental Learning with Mixture Probability Modeling for Dynamic Optimization Problems

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

作者： Lancucki, Adrian Chorowski, Jan Michalak, Krzysztof Filipiak, Patryk Lipinski, Piotr Univ Wroclaw Inst Comp Sci Computat Intelligence Res Grp PL-51151 Wroclaw Poland Wroclaw Univ Econ Inst Business Informat Wroclaw Poland

ISBN: (纸本)9783319108407;9783319108391

This paper proposes a multimodal extension of PBILC based on Gaussian mixture models for solving dynamic optimization problems. By tracking multiple optima, the algorithm is able to follow the changes in objective functions more efficiently than in the unimodal case. The approach was validated on a set of synthetic benchmarks including Moving Peaks, dynamization of the Rosenbrock function and compositions of functions from the IEEE CEC'2009 competition. The results obtained in the experiments proved the efficiency of the approach in solving dynamic problems with a number of competing peaks.

关键词： evolutionary algorithms estimation of distribution algorithms dynamic optimization problems multimodal optimization

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An AI-based system for pricing diverse products and services

引用

29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

作者： Shakya, S. Chin, C. M. Owusu, G. BT Innovate & Design Intelligent Syst Res Ctr Ipswich IP5 3RE Suffolk England

ISBN: (纸本)9781848829824

This paper describes an applied research work that looks at different ways to effectively manage resources Particularly, it describes how revenue management techniques can be used to balance demand against capacity, and describes a system that uses different OR and AI techniques to intelligently price diverse products and services This system can produce pricing policies for wide range of products and services regardless of the model of demand used The system incorporates a model specification layer, which provides flexibility in defining the demand model for different products It also incorporates an optimisation layer, which takes the specified model as an input and produces the pricing and production guidelines for the product The system can be either used as a stand alone system or can be incorporated as a generic modelling and optimisation component within a larger revenue management system (C) 2009 Elsevier B V All rights reserved

关键词： estimation of distribution algorithms Genetic algorithm Evolutionary algorithm Evolutionary computation Probabilistic graphical models Artificial intelligence

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Relational Bayesian Optimization for Permutation

Relational Bayesian Optimization for Permutation

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Huang, Bo-Wei Fang, Wen-Zhong Liao, Hsu-Chen Yu, Tian-Li Natl Taiwan Univ Taiwan Evolutionary Intelligence Lab Taipei Taiwan

ISBN: (纸本)9798400701207

Relational Bayesian optimization for permutation (RBOP) is a new permutation estimation of distribution algorithm proposed in this paper. RBOP uses binary relations to represent the common property in permutations. Inspired by the Bayesian optimization algorithm, RBOP first builds a Bayesian network using binary relations. Then, RBOP samples genes using the most certain edge in the Bayesian network. In the scenario of black-box optimization, RBOP aims to solve various permutation problems with a limited number of function evaluations. Experiments show that in terms of average relative percentage deviation, RBOP outperforms edge histogram-based sampling algorithm on quadratic assignment problems, permutation flowshop problems and linear ordering problems. Additionally, RBOP also outperforms both node histogram-based sampling algorithm and kernels of Mallows model using Cayley distance on traveling salesman problems, permutation flow shop problems, linear ordering problems and 6 out of 10 instances of quadratic assignment problems.

关键词： permutation estimation of distribution algorithms binary relation

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Trimming, Ordering, and Similarity Check for DSMGA-II: DSMGA-II-TOS 22

Trimming, Ordering, and Similarity Check for DSMGA-II: DSMGA...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Ching-Chung, Huang Tian-Li, Yu Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9781450392686

The dependency structure matrix genetic algorithm II (DSMGA-II) is one of the state-of-the-art model-building genetic algorithms capable of solving combinatorial optimization problems by exploiting the underlying structures of the problems. The linkage model generates a series of masks for trial to recombine genes among chromosomes via optimal mixing. This paper proposes three improvements that adaptively adjust the scope, the order, and the receivers of trials. Specifically, the mean of the mask sizes from previous successful recombinations is used to limit the maximum sizes of later trials. Also, successful recombinations prioritize the corresponding mask sizes of trials. Finally, recombinations are confined between chromosomes that pass the proposed similarity check. The ablation study indicates that each proposed technique is indispensable. Combined with these three improvements, DSMGA-II-2E empirically consumes fewer function evaluations on most of the test problems.

关键词： genetic algorithms model building estimation of distribution algorithms optimal mixing

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Solving Sudoku Puzzles with Node Based Coincidence Algorithm

Solving Sudoku Puzzles with Node Based Coincidence Algorithm

引用

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

作者： Waiyapara, Kiatsopon Wattanapornprom, Warin Chongstitvatana, Prabhas Chulalongkorn Univ Fac Engn Dept Compute Engn Bangkok Thailand Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781479908066;9781479908059

In Evolutionary computation, Sudoku puzzles are categorized as hard combinatorial problems. It is almost impossible to solve these puzzles using only native operations of genetic algorithms. This article presents an application of Coincidence algorithm, which is an estimation of distribution algorithms in the class of evolutionary computation that can outperform traditional algorithms on several combinatorial problems. It makes use of both positive and negative knowledge for solving problems. The proposed method is compared with the current best known method. It significantly outperforms problem-specific GA to solve easy, medium, and hard level of Sudoku puzzles.

关键词： Coincidence Algorithm Sudoku Puzzle Multimodal Optimization estimation of distribution algorithms Genetic Algorithm

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