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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Shibata, Shunsuke Orito, Yukiko Hanada, Yoshiko Yamamoto, Hisashi Tokyo Metropolitan Univ Dept Syst Design Tokyo 1910065 Japan Hiroshima Univ Dept Econ Hiroshima 7398525 Japan Kansai Univ Fac Engn Sci Osaka 5648680 Japan Tokyo Metropolitan Univ Dept Syst Design Tokyo 1910065 Japan

ISBN: (纸本)9781479906529

It is desired to replicate the benchmark portfolio when it has delivered good performances. In this paper, our focus is on the portfolio replication problem that the total return of the benchmark portfolio is opened to the public but the proportion-weighted combination is closed to the public. It is difficult to solve this replication problem because we cannot have any techniques to solve the simultaneous equations when the number of unknown valuables is more than the number of equations. In order to solve such a problem, we propose the new estimation of distribution algorithm with the operation switching two distributions in this paper. In the numerical experiments, we show that the portfolios replicated by our proposing algorithm have delivered good performances even in the future periods.

关键词： Long-Short Portfolio Replication estimation of distribution algorithm Switching distributions

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Maximizing the Number of Polychronous Groups in Spiking Networks 12

Maximizing the Number of Polychronous Groups in Spiking Netw...

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

作者： Santana, Roberto Bielza, Concha Larranaga, Pedro Univ Basque Country UPV EHU P Manuel de Lardizabal 1 Donostia San Sebastian 20018 Spain Univ Politecn Madrid DIA Madrid Spain

ISBN: (纸本)9781450311786

In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our results indicate that the introduced approach can be used to considerably increase the number of such groups.

关键词： polychronization spiking neural network estimation of distribution algorithm

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Dependency Trees, Permutations, and Quadratic Assignment Problem 07

Dependency Trees, Permutations, and Quadratic Assignment Pro...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Pelikan, Martin Tsutsui, Shigeyoshi Kalapala, Rajiv Univ Missouri Dept Math & Comp Sci St Louis MO 63121 USA Hannan Univ Dept Management & Informat Sci Matsubara Osaka Japan

ISBN: (纸本)9781595936974

This paper describes and analyzes an estimation of distribution algorithm based on dependency tree models (dtEDA), which can explicitly encode probabilistic models for permutations. dtEDA is tested on deceptive ordering problems and a number of instances of the quadratic assignment problem. The performance of dtEDA is compared to that of the standard genetic algorithm with the partially matched crossover (PMX) and the linear order crossover (LOX). In the quadratic assignment problem, the robust tabu search is also included in the comparison.

关键词： estimation of distribution algorithm dependency tree permutation domain quadratic assignment problem deceptive ordering problem linkage learning

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Non-integrated algorithm based on EDA and Tabu Search for Test Task Scheduling Problem 51

Non-integrated Algorithm based on EDA and Tabu Search for Te...

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51st Annual IEEE AUTOTESTCON Conference

作者： Lu, Hui Zhang, Mengmeng Beihang Univ Sch Elect & Informat Engn Xueyuan Rd 37 Beijing 100191 Peoples R China

ISBN: (纸本)9781479981908

The optimization of test task scheduling problem (TTSP) is an important issue in automatic test system (ATS). TTSP is a complex combination optimization problem and includes two sub-problems. They are test task sequencing and test scheme combination. According to the characteristic of TTSP, a non-integrated algorithm based on estimation of distribution algorithm and Tabu Search (EDA-TS) is proposed in this paper. EDA focuses on solving test task sequencing in global searching, and TS emphasizes on solving test scheme combination in local searching. In addition, we give a mathematical model for TTSP. We prove that TTSP is an NP-hard by using traveling salesman problem (TSP) based on the mathematical model. The statistical results of single objective TTSP suggest that our approach has a stronger searching ability and good convergence compared with other three popular algorithms. The experiments of the multi-objectives TTSP also illustrate that EDA-TS has a strong searching ability and can maintain a diversity of solutions.

关键词： test task scheduling problem NP-hard estimation of distribution algorithm Tabu Search

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Univariate Marginal distribution algorithm in Combination with Extremal Optimization (EO, GEO)

Univariate Marginal Distribution Algorithm in Combination wi...

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18th International Conference on Neural Information Processing (ICONIP)

作者： Hashemi, Mitra Meybodi, Mohammad Reza Islamic Azad Univ Dept Comp Engn & Informat Technol Qazvin Branch Qazvin Iran Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran

ISBN: (纸本)9783642249570;9783642249587

The UMDA algorithm is a type of estimation of distribution algorithms. This algorithm has better performance compared to others such as genetic algorithm in terms of speed, memory consumption and accuracy of solutions. It can explore unknown parts of search space well. It uses a probability vector and individuals of the population are created through the sampling. Furthermore. EO algorithm is suitable for local search of near global best solution in search space. and it dose not stuck in local optimum. Hence, combining these two algorithms is able to create interaction between two fundamental concepts in evolutionary algorithms, exploration and exploitation. and achieve better results of this paper represent the performance of the proposed algorithm on two NP-hard problems, multi processor scheduling problem and graph hi-partitioning problem.

关键词： Univariate Marginal distribution algorithm Extremal Optimization Generalized Extremal Optimization estimation of distribution algorithm

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Probabilistic distribution models for EDA-based GP 05

Probabilistic distribution models for EDA-based GP

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Genetic and Evolutionary Computation Conference

作者： Yanai, Kohsuke Iba, Hitoshi Univ Tokyo Grad Sch Frontier Sci Dept Frontier Informat Kashiwa Chiba 2778561 Japan

ISBN: (纸本)1595930108

This paper proposes a novel technique for a program evolution based on probabilistic models. In the proposed method, two probabilistic distribution models with probabilistic dependencies between variables are used together. We empirically comfirm that our proposed method has higher search performance. Thereafter, we discuss the effectiveness of its distribution models.

关键词： algorithms genetic programming program evolution evolutionary computing estimation of distribution algorithm probabilistic model-building genetic algorithm

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Learning probability distributions in continuous evolutionary algorithms - A comparative review

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Natural Computing 2004年第1期3卷 77-112页

作者： Kern, Stefan Müller, Sibylle D. Hansen, Nikolaus Büche, Dirk Ocenasek, Jiri Koumoutsakos, Petros Institute of Computational Science Swiss Federal Institute of Technol. CH-8092 Zürich Switzerland Computational Laboratory Swiss Federal Institute of Technol. CH-8092 Zürich Switzerland

We present a comparative review of Evolutionary algorithms that generate new population members by sampling a probability distribution constructed during the optimization process. We present a unifying formulation for five such algorithms that enables us to characterize them based on the parametrization of the probability distribution, the learning methodology, and the use of historical information. The algorithms are evaluated on a number of test functions in order to assess their relative strengths and weaknesses. This comparative review helps to identify areas of applicability for the algorithms and to guide future algorithmic developments. © 2004 Kluwer Academic Publishers.

关键词： Adaptation Bayesian optimization estimation of distribution algorithm Evolution strategy Evolutionary algorithm learning probability distribution

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A Hybrid Adaptive Evolutionary algorithm in the Domination-based and Decomposition-based Frameworks of Multi-objective Optimization

A Hybrid Adaptive Evolutionary Algorithm in the Domination-b...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Shim, V. A. Tan, K. C. Tan, K. K. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9781467315098

Under the framework of evolutionary paradigms, many variations of evolutionary algorithms have been designed. Each of the algorithms performs well in certain cases and none of them are dominating one another. This study is based on the idea of synthesizing different evolutionary algorithms so as to complement the limitations of each algorithm. On top of this idea, this paper proposes an adaptive mechanism that synthesizes a genetic algorithm, differential evolution and estimation of distribution algorithm. The adaptive mechanism takes into account the ratio of the number of promising solutions generated from each optimizer in an early stage of evolutions so as to determine the proportion of the number of solutions to be produced by each optimizer in the next generation. Furthermore, the adaptive algorithm is also hybridized with the evolutionary gradient search to further enhance its search ability. The proposed hybrid adaptive algorithm is developed in the domination-based and decomposition-based multi-objective frameworks. An extensive experimental study is carried out to test the performances of the proposed algorithms in 38 state-of-the-art benchmark test instances.

关键词： Decomposition differential evolution domination genetic algorithm estimation of distribution algorithm evolutionary gradient search hybrid multi-objective optimization

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EDAs cannot be Balanced and Stable 16

EDAs cannot be Balanced and Stable

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Friedrich, Tobias Koetzing, Timo Krejca, Martin S. Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781450342063

estimation of distribution algorithms (EDAs) work by iteratively updating a distribution over the search space with the help of samples from each iteration. Up to now, theoretical analyses of EDAs are scarce and present run time results for specific EDAs. We propose a new framework for EDAs that captures the idea of several known optimizers, including PBIL, UMDA, A-MMASIB, cGA, and (1,lambda)-EA. Our focus is on analyzing two core features of EDAs: a balanced EDA is sensitive to signals in the fitness;a stable EDA remains uncommitted under a biasless fitness function. We prove that no EDA can be both balanced and stable. The LEADINGONES function is a prime example where, at the beginning of the optimization, the fitness function shows no bias for many bits. Since many well-known EDAs are balanced and thus not stable, they are not well-suited to optimize LEADINGONES. We give a stable EDA which optimizes LEADINGONES within a time of O(n log n).

关键词： estimation of distribution algorithm LEADINGONES theory run time analysis

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An EDA-based Genetic algorithm for EV Charging Scheduling under Surge Demand

An EDA-based Genetic Algorithm for EV Charging Scheduling un...

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IEEE International Conference on Services Computing (IEEE SCC)

作者： Li, Tianyang Li, Xiaolong He, Ting Zhang, Yufeng Northeast Elect Power Univ Sch Comp Sci Jilin Jilin Peoples R China Jiangxi New Energy Technol Inst Nanchang Jiangxi Peoples R China Bank Inner Mongolia Co Ltd Harbin Branch Harbin Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen Peoples R China Univ Birmingham Birmingham Business Sch Birmingham W Midlands England

ISBN: (数字)9781665481465

ISBN: (纸本)9781665481465

With continually increased Electric Vehicles (EVs), the EVs Charging Scheduling is of great importance to managing multiple charging demands for maximizing user satisfactions and minimizing adverse influences on the grid. However, it is challenging to effectively manage EVs charging schedules when a large number of (on-the-move) EVs are planning to charge at the same time. With this concern, we focus on Charging Station (CS)-selection decision making by the global aggregator that is taken as controller to implement charging management for EVs and CSs. An estimation of distribution algorithm (EDA)-based genetic algorithm is proposed to find constrained charging scheduling plans to maximize the charging efficiency, which may improve user satisfaction and alleviate impacts on the grid. Experimental results under a city scenario with realistic EVs and CSs show the advantage of our proposal, in terms of minimized queuing time and maximized charging performance at both the EV and CS sides. The code and data are available at https://***/EV-charging-scheduling-algorithm.

关键词： EV charging charging scheduling estimation of distribution algorithm genetic algorithm surge demand

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