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

共 436 条记录，以下是351-360 订阅

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Evolving NK-complexity for Evolutionary Solvers 12

Evolving NK-complexity for Evolutionary Solvers

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

作者： Santana, Roberto Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU P Manuel Lardizabal 1 San Sebastian 20018 Spain

ISBN: (纸本)9781450311786

In this paper we empirically investigate the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms (EDAs). We evolve instances that maximize the EDA complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measure, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.

关键词： estimation of distribution algorithm NK-model complexity analysis networks

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Probabilistic Model-Building Genetic algorithms 10

Probabilistic Model-Building Genetic Algorithms

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12th Annual Genetic and Evolutionary Computation Conference (GECCO)

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

ISBN: (纸本)9781450300735

Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs).Replacing traditional crossover and mutation operators by building and sampling a probabilistic model of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimization enables the design of optimization techniques that can automatically adapt to the given problem. There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and *** tutorial Probabilistic Model-Building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.

关键词： bayesian network probabilistic model eda optimization genetic algorithm probabilistic model-building genetic algorithm estimation of distribution algorithm evolutionary computation learning

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Expanding variational autoencoders for learning and exploiting latent representations in search distributions 18

Expanding variational autoencoders for learning and exploiti...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Garciarena, Unai Santana, Roberto Mendiburu, Alexander Univ Basque Country Intelligent Syst Grp Donostia San Sebastian Spain

ISBN: (纸本)9781450356183

In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden variables to the models as a more accurate way to represent the search distributions. Recently, a number of neural-network models that compute approximations of posterior (latent variable) distributions have been introduced. In this paper, we investigate the use of the variational autoencoder (VAE), a class of neural-network based generative model, for modeling and sampling search distributions as part of an estimation of distribution algorithm. We show that VAE can capture dependencies between decision variables and objectives. This feature is proven to improve the sampling capacity of model based EAs. Furthermore, we extend the original VAE model by adding a new, fitness-approximating network component. We show that it is possible to adapt the architecture of these models and we present evidence of how to extend VAEs to better fulfill the requirements of probabilistic modeling in EAs. While our results are not yet competitive with state of the art probabilistic-based optimizers, they represent a promising direction for the application of generative models within EDAs.

关键词： machine learning variational autoencoder estimation of distribution algorithm neural network generative modeling

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A New EDA with Dimension Reduction Technique for Large Scale Many-Objective Optimization 11th

A New EDA with Dimension Reduction Technique for Large Scale...

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11th International Conference on Swarm Intelligence (ICSI)

作者： Shi, Mingli Ma, Lianbo Yang, Guangming Northeastern Univ Shenyang Peoples R China

ISBN: (纸本)9783030539559;9783030539566

The performance of many-objective evolutionary algorithms deteriorates appreciably in solving large-scale many-objective optimization problems (MaOPs) which encompass more than hundreds variables. One of the known rationales is the curse of dimensionality. estimation of distribution algorithms sample new solutions with a probabilistic model built from the statistics extracting over the existing solutions so as to mitigate the adverse impact of genetic operators. In this paper, an Gaussian Bayesian network-based estimation of distribution algorithm (GBNEDA-DR) is proposed to effectively tackle continued large-scale MaOPs. In the proposed algorithm, dimension reduction technique (i.e. LPP) is employed in the decision space to speed up the estimation search of the proposed algorithm. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different decision space dimensions, and achieves comparable results on some compared with many existing algorithms.

关键词： estimation of distribution algorithm Gaussian Bayesian network Dimension reduction

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An Orthogonal hybrid algorithm for the resource-constrained project scheduling problem

An Orthogonal hybrid algorithm for the resource-constrained ...

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7th World Congress on Intelligent Control and Automation

作者： Huang, Zhiyu Chinese Acad Sci Inst Automat Lab Complex Syst & Intelligence Sci Beijing 100080 Peoples R China

ISBN: (纸本)9781424421138

As an effective method, the generation-based heuristic algorithm gets information from the history search at one generation, and then uses this information to generate some other solutions as the next generation so that a near optimum solution can be found. To do this effectively, how to represent a solution is a fundamental problem. After providing a new representation for a solution, a hybrid algorithm was given. The algorithm used the ideal of orthogonal design to make the solutions generating more reasonable, and used the ideal of estimation of distribution algorithm to elicit the information about activity linking in a population so that the solutions with history linking information can be generated, and used the ideal of Scatter Search to keep the search process strolling in divers parts, and used the ideal of reverse schedule to improve the fitness of found solutions. The simulation results reveal the efficiency of the algorithm.

关键词： Scatter Search estimation of distribution algorithm Revere Schedule Orthogonal Design Solution Representation

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Ant Colony Optimization Beats Resampling on Noisy Functions

Ant Colony Optimization Beats Resampling on Noisy Functions

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Friedrich, Tobias Koetzing, Timo Quinzan, Francesco Sutton, Andrew M. Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781450343237

Despite the pervasiveness of noise in real-world optimization, there is little understanding of the interplay between the operators of randomized search heuristics and explicit noise handling techniques such as statistical resampling. Ant Colony Optimization (ACO) algorithms are claimed to be particularly well-suited to dynamic and noisy problems, even without explicit noise-handling techniques. In this work, we empirically investigate the trade-offs between resampling an the noise-handling abilities of ACO algorithms. Our main focus is to locate the point where re sampling costs more than it is worth.

关键词： Evolutionary algorithm Genetic algorithm Crossover estimation of distribution algorithm Ant Colony Optimization Robustness Noise

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An EDA-based Community Detection in Complex Networks 7

An EDA-based Community Detection in Complex Networks

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7th International Symposium on Telecommunications (IST)

作者： Parsa, Mohsen Ghassemi Mozayani, Nasser Esmaeili, Ahmad Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781479953592

Communities are basic units of complex networks and understanding of their structure help us to understand the structure of a network. Communities are groups of nodes that have many links inside and few links outside them. Community detection in a network can be modeled as an optimization problem. We can use some measures such as Modularity and Community Score for evaluating the quality of a partition of nodes. In this paper, we present a new algorithm for detecting communities in networks based on an estimation of distribution algorithm (EDA) with the assumption that the problem variables are independent. EDAs are those evolutionary algorithms that build and sample the probabilistic models of selected solutions instead of using crossover and mutation operators. In this paper, we assess our algorithm by synthetic and real data sets and compare it with other community detection algorithms.

关键词： complex networks community detection estimation of distribution algorithm graph mining

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Alternative Representations for Codifying Solutions in Permutation-Based Problems

Alternative Representations for Codifying Solutions in Permu...

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

作者： Malagon, Mikel Irurozki, Ekhine Ceberio, Josu Univ Basque Country Donostia San Sebastian Spain Basque Ctr Appl Math Bilbao Spain

ISBN: (纸本)9781728169293

Since their introduction, estimation of distribution algorithms (EDAs) have proved to be very competitive algorithms to solve many optimization problems. However, despite recent developments, in the case of permutation-based combinatorial optimization problems, there are still many aspects that deserve further research. One of them is the influence of the codification employed to represent the solutions on the overall performance of the algorithm. When considering classical EDAs, optimizing permutation problems is challenging, and specific mechanisms are needed to hold the restrictions associated with the permutation nature of solutions. In this paper, in addition to the permutation-vector codification, we investigate alternative representations to describe solutions of permutation problems in the context of EDAs. In order to evaluate their influence, we adopted a classical EDA and conducted an experimental study on two different permutation problems and representations for codifying solutions. The results revealed a narrow relationship between the type of combinatorial problem optimized and the selected representation used to codify its solutions. Moreover, the results point out that choosing the appropriate representation to codify solutions of the given permutation problem is critical for the performance of the algorithm.

关键词： estimation of distribution algorithm permutation codification inversion vector UMDA

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Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs

Adding Probabilistic Dependencies to the Search of Protein S...

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10th International Conference on Parallel Problem Solving from Nature

作者： Santana, Roberto Larranaga, Pedro Lozano, Jose A. Univ Basque Country Intelligent Syst Grp Paseo Manuel de Lardizabal 1 San Sebastian 20018 Donostia Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence San Sebastian 20018 Donostia Spain Tech Univ Madrid Dept Artificial Intelligence D-28660 Madrid Spain

ISBN: (纸本)9783540876991

The problem of finding an optimal positioning for the side chain residues of a protein is called the side chain placement or side chain prediction problem. It can be posed as an optimization problem in the discrete domain. In this paper we use an estimation of distribution algorithm to address this optimization problem. Using a set of 50 difficult protein instances, it is shown that the addition of dependencies between the variables in the probabilistic model can improve the quality of the solutions achieved for most of the instances considered. However, we also show that only when information about the known interactions between the residues is considered in the creation of the probabilistic model, the addition of the dependencies contributes to improve the quality of the solutions obtained.

关键词： estimation of distribution algorithm protein structure prediction probabilistic models

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An Evolutionary algorithm Based Hyper-heuristic for the Set Packing Problem 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Chaurasia, Sachchida Nand Jung, Donghwi Lee, Ho Min Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 136713 South Korea Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea

ISBN: (纸本)9789811307614;9789811307607

Utilizing knowledge of the problem of interest and lessons learned from solving similar problemswould help to find the final optimal solution of better quality. A hyper-heuristic algorithm is to gain an advantage of such process. In this paper, we present an evolutionary algorithm based hyper-heuristic framework for solving the set packing problem (SPP). The SPP is a typicalNP-hard problem. The hyper-heuristic is comprising of high level and low level. The higher level is mainly engaged in generating or constructing a heuristic. An evolutionary algorithm with guided mutation (EA/G) is employed at the high level. Whereas a set of problem-independent and problem-specific heuristics, called low level heuristics, are employed at the low level of hyper-heuristic. EA/G is recently added to the class of the evolutionary algorithms that try to utilize the complementary characteristics of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) to generate new offspring. In EA/G, the guided mutation operator generates an offspring by sampling the probability vector. The proposed approach is compared with the state-of-the-art approaches reported in the literature. The computational results show the effectiveness of the proposed approach.

关键词： Set packing problem Constrained optimization Genetic algorithm estimation of distribution algorithm Guided mutation Heuristic Hyper-heuristic

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