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

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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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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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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Porting estimation of distribution algorithms to the Cell Broadband Engine

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PARALLEL COMPUTING 2010年第1-11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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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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Integrating estimation of distribution algorithms versus Q-learning into Meta-RaPS for solving the 0-1 multidimensional knapsack problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 112卷 706-720页

作者： Arin, Arif Rabadi, Ghaith Middle East Tech Univ Ankara Turkey Old Dominion Univ Engn Management & Syst Engn Engn Syst BldgRoom 2102 Norfolk VA 23529 USA

Finding near-optimal solutions in an acceptable amount of time is a challenge when developing sophisticated approximate approaches. A powerful answer to this challenge might be reached by incorporating intelligence into metaheuristics. We propose integrating two methods into Meta-RaPS (Metaheuristic for Randomized Priority Search), which is currently classified as a memoryless metaheuristic. The first method is the estimation of distribution algorithms (EDA), and the second is utilizing a machine learning algorithm known as Q-Learning. To evaluate their performance, the proposed algorithms are tested on the 0-1 Multidimensional Knapsack Problem (MKP). Meta-RaPS EDA appears to perform better than Meta-RaPS Q-Learning. However, both showed promising results compared to other approaches presented in the literature for the 0-1 MKP. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Machine learning estimation of distribution algorithms Q-learning Meta-RaPS 0-1 multidimensional knapsack problem

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Evolutionary and estimation of distribution algorithms for Unconstrained, Constrained, and Multiobjective Noisy Combinatorial Optimisation Problems

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EVOLUTIONARY COMPUTATION 2023年第3期31卷 259-285页

作者： Aishwaryaprajna Rowe, Jonathan E. Univ Birmingham Sch Comp Sci Birmingham W Midlands England Alan Turing Inst London England

We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneMax and other linear problems. We find that UMDA and the Paired-Crossover Evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed time budget. In the second stage, UMDA and PCEA are then tested on more complex noisy problems: SubsetSum, Knapsack, and SetCover. Both perform well under increasing levels of noise, with UMDA being the better of the two. In the third stage, we consider two noisy multiobjective problems (CountingOnesCountingZeros and a multiobjective formulation of SetCover). We compare several adaptations of UMDA for multiobjective problems with the Simple Evolutionary Multiobjective Optimiser (SEMO) and NSGA-II. We conclude that UMDA, and its variants, can be highly effective on a variety of noisy combinatorial optimisation, outperforming many other evolutionary algorithms.

关键词： Noisy combinatorial optimisation noisy multiobjective optimisation expected runtime crossover estimation of distribution algorithms NSGA-II

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Space complexity of estimation of distribution algorithms

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EVOLUTIONARY COMPUTATION 2005年第1期13卷 125-143页

作者： Gao, Y Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

In this paper, we investigate the space complexity of the estimation of distribution algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and Bayesian network-based algorithms. Using random additive functions as the prototype, we prove that the space complexity of the factorized distribution algorithm and Bayesian network-based algorithms is exponential in the problem size even if the optimization problem has a very sparse interaction structure.

关键词： estimation of distribution algorithms space complexity additive fitness functions graphical models and bayesian networks treewidth

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Feature selection for intrusion detection using new multi-objective estimation of distribution algorithms

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APPLIED INTELLIGENCE 2019年第12期49卷 4237-4257页

作者： Maza, Sofiane Touahria, Mohamed Univ Ferhat Abbas Setif 1 Dept Comp Sci Setif 19000 Algeria

The manipulation of a large number of features has become a critical problem in Intrusion Detection Systems(IDS). Therefore, Feature Selection (FS) is integrated to select the significant features, in order to avoid the computational complexity, and improve the classification performance. In this paper, we present a new multi-objective feature selection algorithm MOEDAFS (Multi-Objective estimation of distribution algorithms (EDA) for Feature Selection). The MOEDAFS is based on EDA and Mutual Information (MI). EDA is used to explore the search space and MI is integrated as a probabilistic model to guide the search by modeling the redundancy and relevance relations between features. Therefore, we propose four probabilistic models for MOEDAFS. MOEDAFS selects the better feature subsets (non-dominated solutions) that have a better detection accuracy and smaller number of features. MOEDAFS uses two objective functions (minimizing classification Error Rate (ER) and minimizing the Number of Features(NF)). In order to demonstrate the performance of MOEDAFS, a comparative study is designed by internal and external comparison on NSL-KDD dataset. Internal comparison is performed between the four versions of MOEDAFS. External comparison is organized against some well-known deterministic, metaheuristic, and multi-objective feature selection algorithms that have a single and Multi-solution. Experimental results demonstrate that MOEDAFS outperforms recent algorithms.

关键词： Intrusion detection system Feature selection Multi-Objective optimization estimation of distribution algorithms Mutual information

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