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Side chain placement using estimation of distribution algorithms

ARTIFICIAL INTELLIGENCE IN MEDICINE 2007年第1期39卷 49-63页

作者： Santana, Roberto Larranaga, Pedro Lozano, Jose A. Univ Basque Country Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20080 Spain

Objective: This paper presents an algorithm for the solution of the side chain placement problem. Methods and materials: The algorithm combines the application of the Goldstein elimination criterion with the univariate marginal distribution algorithm (UMDA), which stochastically searches the space of possible solutions. The suitability of the algorithm to address the problem is investigated using a set of 425 proteins. Results: For a number of difficult instances where inference algorithms do not converge, it has been shown that UMDA is able to find better structures. Conclusions: The results obtained show that the algorithm can achieve better structures than those obtained with other state-of-the-art methods like inference-based techniques. Additionally, a theoretical and empirical analysis of the computationat cost of the algorithm introduced has been presented. (C) 2006 Elsevier B.V. All rights reserved.

关键词： protein folding estimation of distribution algorithms protein structure prediction rotamers

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Drift and scaling in estimation of distribution algorithms

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EVOLUTIONARY COMPUTATION 2005年第1期13卷 99-123页

作者： Shapiro, JL Univ Manchester Dept Comp Sci Manchester M13 9PL Lancs England

This paper considers a phenomenon in estimation of distribution algorithms (EDA) analogous to drift in population genetic dynamics. Finite population sampling in selection results in fluctuations which get reinforced when the probability model is updated. As a consequence, any probability model which can generate only a single set of values with probability 1 can be an attractive fixed point of the algorithm. To avoid this, parameters of the algorithm must scale with the system size in strongly problem-dependent ways, or the algorithm must be modified. This phenomenon is shown to hold for general EDAs as a consequence of the lack of ergodicity and irreducibility of the Markov chain on the state of probability models. It is illustrated in the case of UMDA, in which it is shown that the global optimum is only found if the population size is sufficiently large. For the needle-in-a haystack problem, the population size must scale as the square-root of the size of the search space. For the one-max problem, the population size must scale as the square-root of the problem size.

关键词： estimation of distribution algorithms probability building genetic algorithms Markov chain analysis convergence analysis needle-in-a-haystack problem counting-ones problem

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A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2012年第1期1卷 103-117页

作者： Ceberio, Josu Irurozki, Ekhine Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Comp Sci & Artificial Intelligence Dept Intelligent Syst Grp Donostia San Sebastian Spain

estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems.

关键词： Evolutionary computation estimation of distribution algorithms Permutation-based optimization problems Probabilistic permutation modelling

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The effectiveness of mutation operation in the case of estimation of distribution algorithms

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BIOSYSTEMS 2007年第2-3期87卷 243-251页

作者： Handa, Hisashi Okayama Univ Fac Engn Dept Informat Technol Okayama 7008530 Japan

The estimation of distribution algorithms are a class of evolutionary algorithms which adopt probabilistic models to reproduce individuals in the next generation, instead of conventional crossover and mutation operators. In this paper, mutation operators are incorporated into estimation of distribution algorithms in order to maintain the diversities in EDA populations. Two kinds of mutation operators are examined: a bitwise mutation operator and a mutation operator taking account into the probabilistic model. In experiments, we do not only compare the proposed methods with conventional EDAs on a few fitness functions but also analyze sampled probabilistic models by using KL-divergence. The experimental results shown in this paper elucidate that the mutation operator taking account into the probabilistic model improve the search ability of EDAs. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

关键词： estimation of distribution algorithms mutation operation KL-divergence

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Copula estimation of distribution algorithms based on exchangeable Archimedean copula

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第1期43卷 13-20页

作者： Wang, Lifang Guo, Xiaodong Zeng, Jianchao Hong, Yi Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China

The two key operators in estimation of distribution algorithms (EDAs) are estimating the distribution model according to the selected population and sampling new individuals from the estimated model. Copula EDA introduces the copula theory into EDA. The copula theory provides the theoretical basis and the way to separate the multivariate joint distribution probability function into a function called copula and the univariate margins. The estimation operator and the sampling operator in copula EDA are discussed in this paper, and three exchangeable Archimedean copulas are used in copula EDA. The experimental results show that the three copula EDAs perform equivalently to some classical EDAs.

关键词： evolutionary algorithms EAs estimation of distribution algorithms EDAs copula EDA copula theory exchangeable Archimedean copula

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An introduction and survey of estimation of distribution algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2011年第3期1卷 111-128页

作者： Hauschild, Mark Pelikan, Martin Univ Missouri Dept Math & Comp Sci Missouri Estimat Distribut Algorithms Lab St Louis MO 63121 USA

estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probabilistic models in optimization offers some significant advantages over other types of metaheuristics. This paper discusses these advantages and outlines many of the different types of EDAs. In addition, some of the most powerful efficiency enhancement techniques applied to EDAs are discussed and some of the key theoretical results relevant to EDAs are outlined. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Stochastic optimization estimation of distribution algorithms Probabilistic models Model building Decomposable problems Evolutionary computation

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Porting estimation of distribution algorithms to the Cell Broadband Engine

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PARALLEL COMPUTING 2010年第10-11期36卷 618-634页

作者： Perez-Miguel, Carlos Miguel-Alonso, Jose Mendiburu, Alexander Univ Basque Country Dept Comp Architecture & Technol Intelligent Syst Grp San Sebastian 20018 Spain

Current consumer-grade computers and game devices incorporate very powerful processors that can be used to accelerate many classes of scientific codes. In this paper we explore the ability of the Cell Broadband Engine to run two similar estimation of distribution algorithms, one for the discrete domain and the other for the continuous domain. Starting from initial, sequential versions, we develop multi-threaded programs for symmetric multiprocessors that are afterwards reworked to run on a Cell-based system. In most cases, the parallel programs significantly accelerate execution times, compared with the sequential counterparts. Additional acceleration is achieved using vector (instead of scalar) operations, which are supported by all the tested platforms. We describe the process of paralle-lizing and porting the programs, and analyze the results obtained taking into account the EDAs under study, the problems solved with them, and the platform in which programs run. We conclude that EDAs are not right targets to be ported to the Cell Broadband Engine. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Parallel computing Cell Broadband Engine estimation of distribution algorithms

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Inexact graph matching by means of estimation of distribution algorithms

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PATTERN RECOGNITION 2002年第12期35卷 2867-2880页

作者： Bengoetxea, E Larrañaga, P Bloch, I Perchant, A Boeres, C Univ Basque Country Dept Comp Architecture & Technol Donostia San Sebastian 20080 Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20080 Spain CNRS URA 820 Ecole Natl Super Telecommun Dept Signal & Image Proc F-75634 Paris 13 France Univ Fed Rio de Janeiro Dept Informat Rio De Janeiro Brazil

estimation of distribution algorithms (EDAs) are a quite recent topic in optimization techniques. They combine two technical disciplines of soft computing methodologies: probabilistic reasoning and evolutionary computing. Several algorithms and approaches have already been proposed by different authors, but up to now there are very few papers showing their potential and comparing them to other evolutionary computational methods and algorithms such as genetic algorithms (GAs). This paper focuses on the problem of inexact graph matching which is NP-hard and requires techniques to find an approximate acceptable solution. This problem arises when a nonbijective correspondence is searched between two graphs. A typical instance of this problem corresponds to the case where graphs are used for structural pattern recognition in images. EDA algorithms are well suited for this type of problems. This paper proposes to use EDA algorithms as a new approach for inexact graph matching. Also, two adaptations of the EDA approach to problems with constraints are described as two techniques to control the generation of individuals, and the performance of EDAs for inexact graph matching is compared with the one of GAs. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： inexact graph matching estimation of distribution algorithms Bayesian networks genetic algorithms hybrid soft computing probabilistic reasoning evolutionary computing

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A review of distances for the Mallows and Generalized Mallows estimation of distribution algorithms

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2015年第2期62卷 545-564页

作者： Ceberio, Josu Irurozki, Ekhine Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp Donostia San Sebastian Spain Univ Basque Country UPV EHU Dept Comp Architecture & Technol Intelligent Syst Grp Donostia San Sebastian Spain

The Mallows (MM) and the Generalized Mallows (GMM) probability models have demonstrated their validity in the framework of estimation of distribution algorithms (EDAs) for solving permutation-based combinatorial optimisation problems. Recent works, however, have suggested that the performance of these algorithms strongly relies on the distance used under the model. The goal of this paper is to review three common distances for permutations, Kendall's-, Cayley and Ulam, and compare their performance under MM and GMM EDAs. Moreover, with the aim of predicting the most suitable distance for solving any given permutation problem, we focus our attention on the relation between these distances and the neighbourhood systems in the field of local search optimisation. In this sense, we demonstrate that the performance of the MM and GMM EDAs is strongly correlated with that of multistart local search algorithms when using related neighbourhoods. Furthermore, by means of fitness landscape analysis techniques, we show that the suitability of a distance to solve a problem is clearly characterised by the generation of high smoothness fitness landscapes.

关键词： Distances Mallows and Generalized Mallows models estimation of distribution algorithms Permutations-based Problems Neighbourhood Landscape

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Effective search for genetic-based machine learning systems via estimation of distribution algorithms and embedded feature reduction techniques

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NEUROCOMPUTING 2013年 113卷 105-121页

作者： Yang, Jiadong Xu, Hua Jia, Peifa Jike Com Beijing 100020 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

Genetic-based machine learning (GBML) systems, which employ evolutionary algorithms (EAs) as search mechanisms, evolve rule-based classification models to represent target concepts. Compared to Michigan-style GBML, Pittsburgh-style GBML is expected to achieve more compact solutions. It has been shown that standard recombination operators in EAs do not assure an effective evolutionary search to solve sophisticated problems that contain strong interactions between features. On the other hand, when dealing with real-world classification tasks, irrelevant features not only complicate the problem but also incur unnecessary matchings in GBML systems, which increase the computational cost a lot. To handle the two problems mentioned above in an integrated manner, a new Pittsburgh-style GBML system is proposed. In the proposed method, classifiers are generated and recombined at two levels. At the high level, classifiers are recombined by rule-wise uniform crossover operators since each classifier consists of a variable-size rule set. At the low level, single rules contained in classifiers are reproduced via sampling Bayesian networks that characterize the global statistical information extracted from promising rules found so far. Furthermore, according to the statistical information in the rule population, an embedded approach is presented to detect and remove redundant features incrementally following the evolution of rule population. Results of empirical evaluation show that the proposed method outperforms the original Pittsburgh-style GBML system in terms of classification accuracy while reducing the computational cost. Furthermore, the proposed method is also competitive to other non-evolutionary, highly used machine learning methods. With respect to the performance of feature reduction, the proposed embedded approach is able to deliver solutions with higher classification accuracy when removing the same number of features as other feature reduction techniques do. (C) 20

关键词： Genetic-based machine learning systems estimation of distribution algorithms Features reduction Evolutionary computation

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