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estimation of distribution algorithms for the computation of innovation estimators of diffusion processes

MATHEMATICS AND COMPUTERS IN SIMULATION 2021年 187卷 449-467页

作者： Arenas, Zochil Gonzalez Jimenez, Juan Carlos Lozada-Chang, Li-Vang Santana, Roberto Univ Estado Rio De Janeiro Dept Appl Math Rio De Janeiro Brazil Inst Cybernet Math & Phys Havana Cuba Freepik Co Dept Business Intelligence Malaga Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Leioa Spain

Innovation Method is a recognized method for the estimation of parameters in diffusion processes. It is well known that the quality of the Innovation Estimator strongly depends on an adequate selection of the initial value for the parameters when a local optimization algorithm is used in its computation. Alternatively, in this paper, we use a strategy based on a modern method for solving global optimization problems, estimation of distribution algorithms (EDAs). We study the feasibility of a specific EDA - a continuous version of the Univariate Marginal distribution Algorithm (UMDAc) - for the computation of the Innovation Estimators. Through numerical simulations, we show that the considered global optimization algorithms substantially improves the effectiveness of the Innovation Estimators for different types of diffusion processes with complex nonlinear and stochastic dynamics. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Innovation Estimators estimation of distribution algorithms Parameter estimation Diffusion process Numerical simulations

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estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第2期44卷 1077-1098页

作者： Soloviev, Vicente P. Larranaga, Pedro Bielza, Concha Univ Politecn Madrid Computat Intelligence Grp Madrid Spain

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

关键词： estimation of distribution algorithms Gaussian Bayesian network Evolutionary algorithms Optimization Industry Environment variables

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estimation of distribution algorithms in Machine Learning: A Survey

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第5期28卷 1301-1321页

作者： Larranaga, Pedro Bielza, Concha Univ Politecn Madrid Dept Inteligencia Artificial Madrid 28660 Spain

The automatic induction of machine learning models capable of addressing supervised learning, feature selection, clustering, and reinforcement learning problems requires sophisticated intelligent search procedures. These searches are usually performed in the possible model structure spaces, leading to combinatorial optimization problems, and in the parameter spaces, where it is necessary to solve continuous optimization problems. This article reviews how the estimation of distribution algorithms, a kind of evolutionary algorithm, can be used to address these problems. Topics include preprocessing, mining association rules, selecting variables, searching for the optimal supervised learning model (both probabilistic and nonprobabilistic models), finding the best hierarchical, partitional, or probabilistic clustering, obtaining the optimal policy in reinforcement learning, and performing inference and structural learning in Bayesian networks for association discovery. Interesting guidelines for future work in this area are also provided.

关键词： Bayesian networks combinatorial optimization continuous optimization estimation of distribution algorithms evolutionary algorithms machine learning Bayesian networks combinatorial optimization continuous optimization estimation of distribution algorithms evolutionary algorithms machine learning

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estimation of distribution algorithms with Kikuchi approximations

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EVOLUTIONARY COMPUTATION 2005年第1期13卷 67-97页

作者： Santana, R Inst Cybernet Math & Phys Havana Cuba

The question of finding feasible ways for estimating probability distributions is one of the main challenges for estimation of distribution algorithms (EDAs). To estimate the distribution of the selected solutions, EDAs use factorizations constructed according to graphical models. The class of factorizations that can be obtained from these probability models is highly constrained. Expanding the class of factorizations that could be employed for probability approximation is a necessary step for the conception of more robust EDAs. In this paper we introduce a method for learning a more general class of probability factorizations. The method combines a reformulation of a probability approximation procedure known in statistical physics as the Kikuchi approximation of energy, with a novel approach for finding graph decompositions. We present the Markov Network estimation of distribution Algorithm (MN-EDA), an EDA that uses Kikuchi approximations to estimate the distribution, and Gibbs Sampling (GS) to generate new points. A systematic empirical evaluation of MN-EDA is done in comparison with different Bayesian network based EDAs. From our experiments we conclude that the algorithm can outperform other EDAs that use traditional methods of probability approximation in the optimization of functions with strong interactions among their variables.

关键词： estimation of distribution algorithms factorized distribution algorithms Kikuchi approximations factorizations graphical models

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estimation of distribution algorithms For Job Schedule Problem

Estimation of Distribution Algorithms For Job Schedule Probl...

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2nd International Conference on Information and Computing Science

作者： Li, Jun Jiang, Yong Nanjing Univ Sci & Technol Dept Informat & Comp Sci Nanjing 210094 Peoples R China

ISBN: (纸本)9780769536347

Well known job schedule problem is NP-hard in management and operating system. A few of evolution algorithms such as GA have been proposed to approach this kind of problems. At present estimation of distribution algorithms are the leading heuristic algorithms which overcome the defect of tradition GA and reflex more excellent property than others. In this paper effective of EDA are tested by the job scheduling problem and the simulation results are analyzed in details.

关键词： estimation of distribution algorithms job schedule problem

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estimation of distribution algorithms for the Firefighter Problem 1

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17th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

ISBN: (数字)9783319554532

ISBN: (纸本)9783319554532;9783319554525

The firefighter problem is a graph-based optimization problem in which the goal is to effectively prevent the spread of a threat in a graph using a limited supply of resources. Recently, metaheuristic approaches to this problem have been proposed, including ant colony optimization and evolutionary algorithms. In this paper estimation of distribution algorithms (EDAs) are used to solve the FFP. A new EDA is proposed in this paper, based on a model that represents the relationship between the state of the graph and positions that become defended during the simulation of the fire spreading. Another method that is tested in this paper, named EH-PBIL, uses an edge histogram matrix model with the learning mechanism used in the Population-based Incremental Learning (PBIL) algorithm with some modifications introduced in order to make it work better with the FFP. Apart from these two EDAs the paper presents results obtained using two versions of the Mallows model, which is a probabilistic model often used for permutation-based problems. For comparison, results obtained on the same test instances using an Ant Colony Optimization (ACO) algorithm, an Evolutionary Algorithm (EA) and a Variable Neighbourhood Search (VNS) are presented. The state-position model proposed in this paper works best for graphs with 1000 vertices and more, outperforming the comparison methods. For smaller graphs (with less than 1000 vertices) the VNS works best.

关键词： estimation of distribution algorithms Graph-based optimization Firefighter problem

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estimation of distribution algorithms based Unmanned Aerial Vehicle Path Planner Using a New Coordinate System

Estimation of Distribution Algorithms based Unmanned Aerial ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Yang, Peng Tang, Ke Lozano, Jose A. Sch Comp Sci & Technol USTC USTC Birmingham Joint Res Inst Intelligent Comput Hefei Peoples R China Univ Basque Country Intelligent Syst Grp Gipuzkoa Spain

ISBN: (纸本)9781479914883

Path planning technique is vital to Unmanned Aerial Vehicle (UAV). Evolutionary algorithms (EAs) have been widely used in planning path for UAV. In these EA-based path planners, Cartesian coordinate system and polar coordinate system are commonly used to codify the path. However, either of them has its drawback: Cartesian coordinate systems result in an enormous search space, whilst polar coordinate systems are unfit for local modifications resulting e.g., from mutation and/or crossover. In order to overcome these two drawbacks, we solve the UAV path planning in a new coordinate system. As the new coordinate system is only a rotation of Cartesian coordinate system, it is inherently easy for local modification. Besides, this new coordinate system has successfully reduced the search space by explicitly dividing the mission space into several subspaces. Within this new coordinate system, an estimation of distribution algorithms (EDAs) based path planner is proposed in this paper. Some experiments have been designed to test different aspects of the new path planner. The results show the effectiveness of this planner.

关键词： Unmanned Aerial Vehicle off-line path planning rotated coordinate system estimation of distribution algorithms

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Guidelines for developing effective estimation of distribution algorithms in solving single machine scheduling problems

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第9期37卷 6441-6451页

作者： Chen, Shih-Hsin Chen, Min-Chih Chang, Pei-Chann Zhang, Qingfu Chen, Yuh-Min Yuan Ze Univ Dept Informat Management Tao Yuan 32026 Taiwan Nanhua Univ Dept Elect Commerce Management Chiayi 62248 Taiwan Natl Cheng Kung Univ Inst Mfg Engn Tainan 70101 Taiwan Univ Essex Dept Comp & Elect Syst Colchester CO4 3SQ Essex England

The goal of this research is to deduce important guidelines for designing effective estimation of distribution algorithms (EDAs). These guidelines will enhance the designed algorithms in balancing the intensification and diversification effects of EDAs. Most EDAs have the advantage of incorporating probabilistic models which can generate chromosomes with the non-disruption of salient genes. This advantage, however, may cause the problem of the premature convergence of EDAs resulted in the probabilistic models no longer generating diversified solutions. In addition, due to overfitting of the search space, probabilistic models cannot really represent the general information of the population. Therefore, this research will deduce important guidelines through the convergency speed analysis of EDAs under different computational times for designing effective EDA algorithms. The major idea is to increase the population diversity gradually by hybridizing EDAs with other meta-heuristics and replacing the procedures of sampling new solutions. According to that, this research further proposes an Adaptive EA/G to improve the performance of EA/G. The proposed algorithm solves the single machine scheduling problems with earliness/tardiness cost in a just-in-time scheduling environment. The experimental results indicated that the Adaptive EA/G outperforms ACGA and EA/G statistically significant in different stopping criteria. This paper, hence, is of importance in the field of EDAs as well as for the researchers in studying the scheduling problems. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： estimation of distribution algorithms Intensification Diversification Single machine scheduling problems Just-in-time

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Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2009年第3期50卷 541-560页

作者： delaOssa, Luis Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Dept Sistemas Informat Albacete 02071 Spain Univ Castilla La Mancha Escuela Politecn Super SIMD i3 Albacete 02071 Spain

The WCOR methodology makes use of metaheuristic algorithms to find the best set of rules, as well as their weights, when learning weighted linguistic fuzzy systems from data. Although in early work based on this approach the search was carried out by means of a genetic algorithm, any other technique can be used. estimation of distribution algorithms (EDAs) are a family of evolutionary algorithms in which the variation operator consists of a probability distribution that is learnt from the best individuals in a population and sampled to generate new ones. There are several possibilities for including problem domain knowledge in EDAs in order to make the search more efficient. In particular, this study examines specifically-designed EDAs which incorporate the information available about the WCOR problem into the probabilistic graphical model used to factorize the probability distribution. The experiments carried out with real and artificial datasets show an improvement in both the results obtained and the computational effort required by the search process. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Linguistic fuzzy systems COR estimation of distribution algorithms Weighted fuzzy rules Soft computing Hybrid systems Data mining

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Keep it unbiased: a comparison between estimation of distribution algorithms and deep learning for human interaction-free side-channel analysis

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JOURNAL OF CRYPTOGRAPHIC ENGINEERING 2024年第3期14卷 499-511页

作者： Rioja, Unai Batina, Lejla Armendariz, Igor Flores, Jose Luis Ikerlan Technol Res Ctr BRTA Arrasate Mondragon Spain Radboud Univ Nijmegen Digital Secur Grp Nijmegen Netherlands

Evaluating side-channel analysis (SCA) security is a complex process, involving applying several techniques whose success depends on human engineering. Therefore, it is crucial to avoid a false sense of confidence provided by non-optimal (failing) attacks. Different alternatives have emerged lately trying to mitigate human dependency, among which deep learning (DL) attacks are the most studied today. DL promise to simplify the procedure by e.g. evading the need for point of interest (POI) selection, among other shortcuts. However, including DL in the equation comes at a price, since working with neural networks is not straightforward in this context. Recently, an alternative has appeared with the potential to mitigate this dependence without adding extra complexity: estimation of distribution algorithm-based SCA. From the perspective of avoiding the need for POI selection, in this paper we provide a comparison of the two relevant methods. Our findings are supported by experimental results on various datasets.

关键词： Hardware security Side-channel analysis Machine learning estimation of distribution algorithms Artificial intelligence Evaluation

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