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2010 IEEE World Congress on Computational Intelligence

作者： Ochoa, Alberto Tejera, Manuel Soto, Marta Inst Cybernet Math & Phys Havana Cuba Assembled New Technol SL Valencia Valencia Spain Inst Cybernet Math & Phys Dept Interdisciplinary Math Havana Cuba

ISBN: (纸本)9781424481262

This paper introduces a novel method for ellipse detection that is based on Estimation of distribution algorithms. The main contribution is the construction of a new fitness function model that in contrast to existing methods can assign positive evaluations to ellipses that do not exist in the image. This approach produces much smoother fitness landscapes increasing the efficiency of the search. A preliminary study of the role of mutation in dealing with the natural multimodality of the ellipse detection problem is also presented.

关键词： computer vision distribution algorithm ellipse detection evolutionary computation feature extraction fitness function model search efficiency search problems

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Bivariate empirical and n-variate Archimedean copulas in Estimation of distribution algorithms

Bivariate empirical and n-variate Archimedean copulas in Est...

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2010 IEEE World Congress on Computational Intelligence

作者： Cuesta-Infante, Alfredo Santana, Roberto Hidalgo, J. Ignacio Bielza, Concha Larranaga, Pedro Univ Complutense Madrid Felipe Coll 2 Capitan 39 Aranjuez Spain Univ Politecn Madrid Madrid Spain Univ Politecn Madrid Sch Comp Sci Madrid Spain Univ Politecn Madrid Dept Comp Struct & Arquitecture DACYA Madrid Spain

ISBN: (纸本)9781424481262

This paper investigates the use of empirical and Archimedean copulas as probabilistic models of continuous estimation of distribution algorithms (EDAs). A method for learning and sampling empirical bivariate copulas to be used in the context of n-dimensional EDAs is first introduced. Then, by using Archimedean copulas instead of empirical makes possible to construct n-dimensional copulas with the same purpose. Both copula-based EDAs are compared to other known continuous EDAs on a set of 2 4 functions and different number of variables. Experimental results show that the proposed copula-based EDAs achieve a better behaviour than previous approaches in a 20% of the benchmark functions.

关键词： continuous estimation distributed algorithms distribution algorithm empirical bivariate copula estimation theory evolutionary computation learning n-dimensional copula n-variate Archimedean copula probabilistic model probability

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A NOVEL TASK RESOURCE distribution algorithm BASED ON MDLS AND GA

A NOVEL TASK RESOURCE DISTRIBUTION ALGORITHM BASED ON MDLS A...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： JIANG-FENG WANG WEI-DONG BAO MAO-JUN ZHANG College of Information Systems & Management National University of Defense Technology Changsha 410073 China

In this paper, we proposed a novel task resource distribution algorithm based on Multidimensional Dynamic List Scheduling and Genetic *** mathematical formulation and implementation of the inner and outer module of th... 详细信息

关键词： distribution algorithm Group technology MDLS algorithm Genetic algorithm

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Evoptool: an Extensible Toolkit for Evolutionary Optimization algorithms Comparison

Evoptool: an Extensible Toolkit for Evolutionary Optimizatio...

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2010 IEEE World Congress on Computational Intelligence

作者： Valentini, Gabriele Malago, Luigi Matteucci, Matteo Politecn Milan DEI I-20133 Milan Italy

ISBN: (纸本)9781424481262

This paper presents Evolutionary Optimization Tool (Evoptool), an optimization toolkit that implements a set of meta-heuristics based on the Evolutionary Computation paradigm. Evoptool provides a common platform for the development and test of new algorithms, in order to facilitate the performance comparison activity. The toolkit offers a wide set of benchmark problems, from classical toy examples to complex tasks, and a collection of implementations of algorithms from the Genetic algorithms and Estimation of distribution algorithms paradigms. Evoptool is flexible and easy to extend, also with algorithms based on other approaches that go beyond Evolutionary Computation.

关键词： Evolutionary Optimization Tool Evoptool toolkit algorithm development algorithm testing distribution algorithm evolutionary computation evolutionary optimization algorithm comparison genetic algorithm genetic algorithms optimization toolkit

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Probabilistic Model Building Genetic Network Programming Using Multiple Probability Vectors

Probabilistic Model Building Genetic Network Programming Usi...

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IEEE Region 10 Conference on TENCON 2010

作者： Li, Xianneng Mabu, Shingo Mainali, Manoj K. Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781424468904

As an extension of GA and GP, a new evolutionary algorithm named Genetic Network Programming (GNP) has been proposed. GNP uses the directed graph structure to represent its solutions, which can express the dynamic environment efficiently. The reusable nodes of GNP can construct compact structures, leading to a good performance in complex problems. In addition, a probabilistic model building GNP named GNP with Estimation of distribution algorithm (GNP-EDA) has been proposed to improve the evolution efficiency. GNP-EDA outperforms the conventional GNP by constructing a probabilistic model by estimating the probability distribution from the selected elite individuals of the previous generation. In this paper, a probabilistic model building GNP with multiple probability vectors (PMBGNP(M)) is proposed. In the proposed algorithm, multiple probability vectors are used in order to escape from premature convergence, and genetic operations like crossover and mutation are carried out to the probability vectors to maintain the diversities of the populations. The proposed algorithm is applied to the controller of autonomous robots and its performance is evaluated.

关键词： autonomous robot controller convergence directed graph directed graphs distribution algorithm evolutionary algorithm genetic algorithms genetic network programming mobile robots multiple probability vector probabilistic logic probabilistic model statistic

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Detecting General Problem Structures with Inductive Linkage Identification

Detecting General Problem Structures with Inductive Linkage ...

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15th Annual International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Huang, Yuan-Wei Chen, Ying-Ping Natl Chiao Tung Univ Dept Comp Sci 1001 Ta Hsueh Rd Hsinchu Taiwan

ISBN: (纸本)9780769542539

Genetic algorithms and the descendant methods have been deemed robust, effective, and practical for the past decades. In order to enhance the features and capabilities of genetic algorithms, tremendous effort has been invested within the research community. One of the major development trends to improve genetic algorithms is trying to extract and exploit the relationship among decision variables, such as estimation of distribution algorithms and perturbation-based methods. In this study, we make an attempt to enable a perturbation-based method, inductive linkage identification (ILI), to detect general problem structures, in which one decision variable can link to an arbitrary number of other variables. Experiments on circular problem structures composed of order-4 and order-5 trap functions are conducted. The results indicate that the proposed technique requires a population size growing logarithmically with the problem size as the original ILI does on non-overlapping building blocks as well as that the population requirement is insensitive to the problem structure consisting of similar substructures as long as the overall problem size is identical.

关键词： circular problem structures decision theory decision variable descendant method distributed algorithms distribution algorithm evolutionary computation general problem structure detection genetic algorithm genetic algorithm genetic algorithms inductive lin

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Optimal Assignment of Delivery Capacity for Network with Constraint

Optimal Assignment of Delivery Capacity for Network with Con...

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Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)

作者： Ming LUO School of Information Science and Technology Xiamen University Xiamen China

For scale-free networks, traffic flow moving freely on a complex network is important to its normal and efficient functioning. In order to enhance the network efficiency, for a capacity-limited network, in this paper, we propose a new distribution algorithm by introducing an adjustable parameter β to control and distribute the delivery capacity of single node dynamically. The simulations reveal that there is an optimal parameter to make the delivery efficiency best. Finally, based on the relationship between degree and load distribution (we define load at each node as the accumulated total number of packets passing through that node when every pair of nodes send and receive a packet along the shortest paths), further analysis about the effectiveness is presented.

关键词： complez network capacity-limited delivery distribution algorithm efficiency

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A new estimation of distribution algorithm based on learning automata

A new estimation of distribution algorithm based on learning...

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IEEE Congress on Evolutionary Computation

作者： Rastegar, R Meybodi, MR Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran

ISBN: (纸本)0780393635

In this paper we introduce an estimation of distribution algorithm based on a team of learning automata. The proposed algorithm is a model based search optimization method that uses a team of learning automata as a probabilistic model of high quality solutions seen in the search process. Simulation results show that the proposed algorithm is a good candidate for solving optimization problems.

关键词： learning automata optimisation search problems distribution algorithm model-based search optimization

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A review of estimation of distribution algorithms in bioinformatics

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BIODATA MINING 2008年第1期1卷 6-6页

作者： Armananzas, Ruben Inza, Inaki Santana, Roberto Saeys, Yvan Luis Flores, Jose Antonio Lozano, Jose Van de Peer, Yves Blanco, Rosa Robles, Victor Bielza, Concha Larranaga, Pedro Univ Basque Country Dept Comp Sci & Artificial Intelligence San Sebastian Spain Univ Ghent Dept Plant Syst Biol B-9000 Ghent Belgium Univ Ghent Dept Mol Genet B-9000 Ghent Belgium Univ Publ Navarra Dept Stat & Operat Res Pamplona Spain Univ Politecn Madrid Dept Arquitectura & Tecnol Sistemas Informat Madrid Spain Univ Politecn Madrid Dept Inteligencia Artificial Madrid Spain

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.

关键词： Bayesian Network Protein Structure Prediction distribution algorithm Feature Subset Selection Bayesian Optimization algorithm

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Load Balancing in Distributed Autonomous Cooperative Systems

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Systems and Computers in Japan 2000年第6期31卷 74-88页

作者： Suguri, Toshihiko Yamashita, Hiroyuki Kinoshita, Shingo Okada, Yasushi NTT Info. and Commun. Syst. Labs. Yokosuka 239-0847 Japan NTT Netwk. Strategy Plan. Department Shinjuku Tokyo 163-8019 Japan

There have been many studies of load balancing in computer systems, for which various algorithms have been proposed. The load balancing methods to date can largely be divided into two types. In the first method, the load information is exchanged among the nodes, and the loads of the nodes are balanced by transferring the request calls. In the second method, the load balancing scheduler in the system monitors the load state in each node, and allocates request calls so that the loads of the nodes are balanced. In contrast to those methods, this paper proposes a load balancing method in a distributed autonomous cooperative system, where the nodes operate asynchronously according to their own decisions. In the proposed method the scheduler is not necessary, and load information is not exchanged or request calls transferred among the nodes. As a result of evaluation by simulation, it is seen that the proposed load balancing method can be applied to a system composed of nodes with uniform performance, as well as to a system composed of nodes with nonuniform performance. The following results were also obtained. (1) In the system composed of nodes with uniform performance, the average response time is less when the load is high, compared to the load balancing methods used to date. (2) In the system composed of nodes with nonuniform performance, the load can be balanced to reduce the average response time not only for the case where only CPU processing is considered in the node, but also for the case where both CPU processing and IO processing are considered. © 2000 Scripta Technica.

关键词： load distribution load balancing distributed system distribution algorithm autonomous cooperative control

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