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

共 309 条记录，以下是221-230 订阅

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PASCAL: An EDA for Parameterless Shape-Independent Clustering

PASCAL: An EDA for Parameterless Shape-Independent Clusterin...

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

作者： Cagnini, Henry E. L. Barros, Rodrigo C. Pontificia Univ Catolica Rio Grande do Sul Porto Alegre RS Brazil

ISBN: (纸本)9781509006229

Data clustering is an unsupervised learning task that can be regarded as the combinatorial optimisation NP-hard problem of assigning N objects to one (or more) among k clusters. Most data clustering algorithms require the user to set a number of pre-defined parameters that have decisive impact in the formation of clusters, such as the number of clusters (initial or final), cluster radius, minimum number of objects, and similar parameters. In addition, several clustering algorithms are limited with regard to the shape of clusters that can be found, a limitation usually resulting from the optimisation process performed over a given distance metric. In this work, we propose a novel clustering algorithm that addresses the two aforementioned problems regarding the amount of parameters and cluster shape. Our approach makes use of the theory of estimation of distribution algorithms in order to probabilistic sample a set of must-link/cannot link constraints in order to generate a data partition with the proper number of clusters. We name our method PASCAL, and we empirically show that it is capable of not only detecting the right number of clusters but also of properly assigning objects to the correct cluster in a variety of artificial and real problems whose solutions are known in advance.

关键词： estimation of distribution algorithms clustering machine learning

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The correlation-triggered adaptive variance scaling IDEA 06

The correlation-triggered adaptive variance scaling IDEA

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8th Annual Genetic and Evolutionary Computation Conference

作者： Grahl, Joern Bosman, Peter A. N. Rothlauf, Franz Mannheim Business Sch Dept Logist D-68131 Mannheim Germany Ctr Math & Comp Sci NL-1090 GB Amsterdam Netherlands Mannheim Business Sch Dept Business Adm & Informat Syst D-68131 Mannheim Germany

ISBN: (纸本)9781595931863

It has previously been shown analytically and experimentally that continuous estimation of distribution algorithms (EDAs) based on the normal pdf can easily suffer from premature convergence. This paper takes a principled first step towards solving this problem. First, prerequisites for the successful use of search distributions in EDAs are presented. Then, an adaptive variance scaling theme is introduced that aims at reducing the risk of premature convergence. Integrating the scheme into the iterated density-estimation evolutionary algorithm (IDEA) yields the correlation-triggered adaptive variance scaling IDEA (CT-AVS-IDEA). The CT-AVS-IDEA is compared to the original IDEA and the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) on a wide range of unimodal test-problems by means of a scalability analysis. It is found that the average number of fitness evaluations grows subiquadratically with the dimensionality, competitively with the CMA-ES. In addition, CT-AVS-IDEA is indeed found to enlarge the class of problems that continuous EDAs can solve reliably.

关键词： evolutionary algorithms estimation of distribution algorithms numerical optimization adaptive variance scaling

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Particle Swarm Optimization with Adaptive Bounds

Particle Swarm Optimization with Adaptive Bounds

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IEEE Congress on Evolutionary Computation (CEC)

作者： El-Abd, Mohammed Kamel, Mohamed S. Amer Univ Kuwait Engn & Sci Div Engn & Sci Div Kuwait Kuwait Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781467315098

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from early attempts to simulate the behavior of birds looking for food. estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms that build and maintain a probabilistic model capturing the search space characteristics and continuously use this model to generate new individuals. In this work, we propose a new PSO and EDA hybrid algorithm that uses the particles' distribution in the search space in order to adjust the search space bounds, hence, restricting the particles movement as well as their allowable maximum velocity. The algorithms is augmented with a mechanism to overcome premature convergence and escape local minima. The algorithm is compared to the standard PSO algorithm using a suite of well-known benchmark optimization functions. Experimental results show that the proposed algorithm has a promising performance.

关键词： Particle swarm optimization estimation of distribution algorithms hybrid techniques non-linear function optimization

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On the Design of Hard mUBQP Instances 16

On the Design of Hard mUBQP Instances

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Zangari, Murilo Santana, Roberto Mendiburu, Alexander Pozo, Aurora Univ Fed Parana Curitiba Parana Brazil Univ Basque Country UPV EHU San Sebastian Spain

ISBN: (纸本)9781450342063

This paper proposes a new method for the design and analysis of multi-objective unconstrained binary quadratic programming (mUBQP) instances, commonly used for testing discrete multi-objective evolutionary algorithms (MOEAs). These instances are usually generated considering the sparsity of the matrices and the correlation between objectives but randomly selecting the values for the matrix cells. Our hypothesis is that going beyond the use of objective correlations by considering different types of variables interactions in the generation of the instances can help to obtain more diverse problem benchmarks, comprising harder instances. We propose a parametric approach in which small building blocks of deceptive functions are planted into the matrices that define the mUBQP. The algorithm for creating the new instances is presented, and the difficulty of the functions is tested using variants of a decomposition-based MOEA. Our experimental results confirm that the instances generated by planting deceptive blocks require more function evaluations to be solved than instances generated using other methods.

关键词： deceptive functions MOEA/D multi-objective optimization multi-objective unconstrained binary quadratic programming PBIL estimation of distribution algorithms

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Probabilistic Model-Building Genetic algorithms 12

Probabilistic Model-Building Genetic Algorithms

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

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

ISBN: (纸本)9781450311786

Probabilistic model-building genetic algorithms (PMBGAs), also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs), guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Building and sampling probabilistic models of promising solutions enables the use of machine learning techniques for automated discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on a similar problem, or to create an efficient computational model of the problem. The tutorial Probabilistic Model-Building GAs provides an introduction to PMBGAs with an overview of major research directions in this area.

关键词： scalable optimization probabilistic model-building genetic algorithms iterated density-estimation algorithms metaheuristics optimization graphical models machine learning evolutionary computation estimation of distribution algorithms

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An Application of EDA and GA to Dynamic Pricing 07

An Application of EDA and GA to Dynamic Pricing

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Shakya, Siddhartha Oliveira, Fernando Owusu, Gilbert BT Grp Chief Technol Off Intelligent Syst Res Ctr Adastral Pk Ipswich IP5 3RE Suffolk England Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England

ISBN: (纸本)9781595936974

E-commerce has transformed the way firms develop their pricing strategies, producing shift, away from fixed pricing to dynamic pricing. In this papers we use two different estimation of distribution algorithms (EDAs), a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm for solving two different dynamic pricing models. Promising results were obtained for an EDA confirming its suitability for resource management in the proposed model. Our analysis gives interesting insights into the application of population based optimization techniques for dynamic pricing.

关键词： estimation of distribution algorithms Dynamic Pricing Evolutionary Computation Resource Management

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Towards Large Scale Continuous EDA: A Random Matrix Theory Perspective 13

Towards Large Scale Continuous EDA: A Random Matrix Theory P...

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15th Genetic and Evolutionary Computation Conference (GECCO)

作者： Kaban, Ata Bootkrajang, Jakramate Durrant, Robert J. Univ Birmingham Sch Comp Sci Edgbaston B15 2TT England

ISBN: (纸本)9781450319638

estimation of distribution algorithms (EDA) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to provide insights about the structure of the search space. However, model building in high dimensions is extremely challenging and as a result existing EDAs lose their strengths in large scale problems. Large scale continuous global optimisation is key to many real-world problems of modern days. Scaling up EAs to large scale problems has become one of the biggest challenges of the field. This paper pins down some fundamental roots of the problem and makes a start at developing a new and generic framework to yield effective EDA-type algorithms for large scale continuous global optimisation problems. Our concept is to introduce an ensemble of random projections of the set of fittest search points to low dimensions as a basis for developing a new and generic divide-and-conquer methodology. This is rooted in the theory of random projections developed in theoretical computer science, and will exploit recent advances of non-asymptotic random matrix theory.

关键词： large scale optimisation estimation of distribution algorithms random projections random matrix theory

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Toward Self-Learning Model-Based EAs 19

Toward Self-Learning Model-Based EAs

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Meulman, Erik A. Bosman, Peter A. N. Ctr Wiskunde & Informat Amsterdam Netherlands Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781450367486

Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostly used to repeatedly optimize different instances of a single problem class, a setting in which specialized algorithms generally perform better. In this paper, we introduce the concept of a new type of MBEA that can automatically specialize its behavior to a given problem class using tabula rasa self-learning. For this, reinforcement learning is a naturally fitting paradigm. A proof-of-principle framework, called SL-ENDA, based on estimation of normal distribution algorithms in combination with reinforcement learning is defined. SL-ENDA uses an RL-agent to decide upon the next population mean while approaching the rest of the algorithm as the environment. A comparison of SL-ENDA to AMaLGaM and CMA-ES on unimodal noiseless functions shows mostly comparable performance and scalability to the broadly used and carefully manually crafted algorithms. This result, in combination with the inherent potential of self-learning model-based evolutionary algorithms with regard to specialization, opens the door to a new research direction with great potential impact on the field of model-based evolutionary algorithms.

关键词： estimation of distribution algorithms machine learning reinforcement learning black-box optimization

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Automated Alphabet Reduction Method with Evolutionary algorithms for Protein Structure Prediction 07

Automated Alphabet Reduction Method with Evolutionary Algori...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Bacardit, Jaume Stout, Michael Hirst, Jonathan D. Sastry, Kumara Llora, Xavier Krasnogor, Natalio Univ Nottingham Sch Comp Sci & IT ASAP Res Grp Jubilee Campus Nottingham NG8 1BB England Univ Nottingham Sch Chem Nottingham NG7 2RD England Univ Illinois Dept Ind & Enterprise Syst Illinois Genet Algorithms Lab IlliGAL Urbana IL 61801 USA Univ Illinois NatlCtr Supercomp Applicat Urbana IL 61801 USA

ISBN: (纸本)9781595936974

This paper focuses on automated procedures to reduce the dimensionality of protein structure prediction datasets by simplifying the way in which the primary sequence of a protein is represented. The potential benefits of this procedure are faster and easier learning process as well as the generation of more compact and human-readable classifiers. The dimensionality reduction procedure we propose consists on the reduction of the, 20-letter amino acid (AA) alphabet, which is normally used to specify a protein sequence, into a lower cardinality alphabet. This reduction comes about by a clustering of AA types accordingly to their physical and chemical similarity. Our automated reduction procedure is guided by a fitness function based on the Mutual Information between the AA-based input attributes of the dataset and the protein structure feature that being predicted. To search for the optimal reduction, the Extended Compact Genetic Algorithm (ECGA) was used, and afterwards the results of this process were fed into (and validated by) BioHEL, a genetics-based machine learning technique. BioHEL used the reduced alphabet to induce rules for protein structure prediction features. BioHEL results are compared to two standard machine learning systems. Our results show that it is possible to reduce the size of the alphabet used for prediction from twenty to just three letters resulting in more compact, i.e. interpretable, rules. Also, a protein-wise accuracy performance measure suggests that the loss of accuracy acrued by this substantial alphabet reduction is not statistically significant when compared to the full alphabet.

关键词： Evolutionary algorithms estimation of distribution algorithms Learning Classifier Systems Rule Induction Bioinformatics Protein Structure Prediction Coordination Number Prediction Alphabet Reduction

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Geometric-Based Sampling For Permutation Optimization 13

Geometric-Based Sampling For Permutation Optimization

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15th Genetic and Evolutionary Computation Conference (GECCO)

作者： Regnier-Coudert, Olivier McCall, John Ayodele, Mayowa Robert Gordon Univ IDEAS Res Inst Aberdeen AB9 1FR Scotland

ISBN: (纸本)9781450319638

There exist several operators to search through permutation spaces that can benefit search and score algorithms when combined. This paper presents COMpetitive Mutating Agents (COMMA), an algorithm which uses geometric mutation operators to create a geometrically defined distribution of solutions. Sampling from the distribution generates solutions in a similar fashion as with estimation of distribution algorithms (EDAs). COMMA is applied on classical permutation optimization benchmarks, namely the Quadratic Assignement and the Permutation Flowshop Scheduling Problems and its performance is compared with those of reference EDAs. Although COMMA does not require a model building step, results suggest that it is competitive with state-of-the-art EDAs. In addition, COMMA's underlying geometric-based sampling could be transposed to representations other than permutations.

关键词： estimation of distribution algorithms Permutation Geometry Quadratic Assignement Problem Flowshop Scheduling Problem

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