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On the convergence of a factorized distribution algorithm with truncation selection

COMPLEXITY 2004年第4期9卷 17-23页

作者： Zhang, QF Univ Essex Dept Comp Colchester CO4 3SQ Essex England

We investigate the global convergence of a factorized distribution algorithm (FDA) with truncation selection. Like conventional genetic algorithms, FDAs maintain and successively improve a population of solutions. In FDAs a distribution model is built based on the statistical information extracted from a set of selected solutions in the current population, and then the model thus built is used to generate new solutions for the next generation. The variable-dependence structure of the distribution model in FDAs is determined by the variable-interaction structure of the objective function. We prove that the FDA with truncation selection converges globally for optimization of a class of additively decomposable functions (ADF). Our results imply that the utilization of appropriately selected dependence relationships is sufficient to guarantee the global convergence of estimation of distribution algorithms (EDAs) for optimization of ADFs. (c) 2004 Wiley Periodicals, Inc.

关键词： genetic algorithms estimation of distribution algorithms factorized distribution algorithms convergence infinite population

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IMPLEMENTATION AND PERFORMANCE EVALUATION OF A PARALLELIZATION OF estimation OF BAYESIAN NETWORK algorithms

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PARALLEL PROCESSING LETTERS 2006年第1期16卷 133-148页

作者： Mendiburu, A. Miguel-Alonso, J. Lozano, J. A. Univ Basque Country Dept Comp Architecture & Technol P M Lardizabal 1 San Sebastian 20018 Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence San Sebastian 20018 Spain

This paper presents, discusses and evaluates parallel implementations of a set of algorithms designed for optimization tasks: estimation of Bayesian Network algorithms (EBNAs). These algorithms belong to the family of Evolutionary Computation. Two different APIs have been combined: message passing and threads, with the aim of obtaining good performance levels in a wide range of parallel machines. Our approach has been to analyze the most computationally intensive sections of sequential implementations of EBNAs, and then to parallelize those sections using a master-worker design pattern. This way the resulting program exhibits exactly the same behavior of the sequential one, but runs faster. To evaluate our proposal, we have chosen a complex scenario where EBNAs can be applied: Feature Subset Selection (FSS) for supervised classification problems. For the experiments, three computing systems have been tested: two different clusters built from commodity components, and an 8-way multiprocessor. Programs have been executed on the target machines using different combination of message-passing processes and threads. Achieved performance is excellent, with efficiency around 1. These encouraging results do widen the spectrum of problems (and problem sizes) that can be solved, in reasonable times, with EBNAs.

关键词： estimation of distribution algorithms Parallel Computing Feature Subset Selection

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Model-based search for combinatorial optimization: A critical survey

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ANNALS OF OPERATIONS RESEARCH 2004年第1-4期131卷 373-395页

作者： Zlochin, M Birattari, M Meuleau, N Dorigo, M Weizmann Inst Sci Dept Appl Math & Comp Sci IL-76100 Rehovot Israel Free Univ Brussels IRIDIA Brussels Belgium NASA Ames Res Ctr Moffett Field CA 94035 USA Tech Univ Darmstadt Intellektik D-64287 Darmstadt Germany

In this paper we introduce model-based search as a unifying framework accommodating some recently proposed metaheuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, cross-entropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method and we propose some extensions.

关键词： ant colony optimization cross-entropy method stochastic gradient ascent estimation of distribution algorithms adaptive optimization metaheuristics

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Model-based evolutionary algorithms: a short survey

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COMPLEX & INTELLIGENT SYSTEMS 2018年第4期4卷 283-292页

作者： Cheng, Ran He, Cheng Jin, Yaochu Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Birmingham Sch Comp Sci Ctr Excellence Res Computat Intelligence & Applic Birmingham B15 2TT W Midlands England

The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given.

关键词： Model-based evolutionary algorithms estimation of distribution algorithms Surrogate modelling Inverse modelling

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A double-distribution statistical algorithm for composite laminate optimization

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2006年第1期31卷 49-59页

作者： Grosset, L LeRiche, R Haftka, RT Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Ecole Mines CNRS UMR 5146 SMS St Etienne France

The paper proposes a new evolutionary algorithm termed Double-distribution Optimization Algorithm (DDOA). DDOA belongs to the family of estimation of distribution algorithms (EDA) that build a statistical model of promising regions of the design space based on sets of good points and use it to guide the search. The efficiency of these algorithms is heavily dependent on the model accuracy. In this work, a generic framework for enhancing the model accuracy by incorporating statistical variable dependencies is presented. The proposed algorithm uses two distributions simultaneously: the marginal distributions of the design variables, complemented by the distribution of physically meaningful auxiliary variables. The combination of the two generates more accurate distributions of promising regions at a low computational cost. The paper demonstrates the efficiency of DDOA for three laminate optimization problems where the design variables are the fiber angles, and the auxiliary variables are integral quantities called lamination parameters. The results show that the reliability of DDOA in finding the optima is greater than that of simple EDA and a standard genetic algorithm, and that its advantage increases with the problem dimension.

关键词： stacking sequence optimization composite laminate evolutionary computation estimation of distribution algorithms lamination parameters

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ECGA vs. BOA in Discovering Stock Market Trading Experts 07

ECGA vs. BOA in Discovering Stock Market Trading Experts

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Lipinski, Piotr Univ Wroclaw Inst Comp Sci PL-50383 Wroclaw Poland

ISBN: (纸本)9781595936974

This paper presents two evolutionary algorithms, ECGA and BOA, applied to constructing stock market trading expertise, which is built on the basis of a set of specific trading rules analysing financial time series of recent price quotations. A few modifications of ECGA are proposed in order to reduce the computing time and make the algorithm applicable for real-time trading. In experiments carried out on real data from the Paris Stock Exchange, the algorithms were compared in terms of the efficiency in solving the optimization problem, in terms of the financial relevance of the investment strategies discovered as well as in terms of the computing time.

关键词： estimation of distribution algorithms extended compact genetic algorithm bayesian optimization algorithm decision support systems stock market expertise financial time series

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Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search 1

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25th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar Conference

作者： Wittenberg, David Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (数字)9783031020568

ISBN: (纸本)9783031020568;9783031020551

Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming (EDA-GP) algorithm that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). At each generation, the idea is to flexibly identify promising properties of the parent population and to transfer these properties to the offspring where the DAE-GP uses denoising to make the model robust to noise that is present in the parent population. Denoising partially corrupts candidate solutions that are used as input to the model. The stronger the corruption, the stronger the generalization of the model. In this work, we study how corruption strength affects the exploration and exploitation behavior of the DAE-GP. For a generalization of the royal tree problem (high-locality problem), we find that the stronger the corruption, the stronger the exploration of the solution space. For the given problem, weak corruption resulting in a stronger exploitation of the solution space performs best. However, in more rugged fitness landscapes (low-locality problems), we expect that a stronger corruption resulting in a stronger exploration will be helpful. Choosing the right denoising strategy can therefore help to control the exploration and exploitation behavior in search, leading to an improved search quality.

关键词： Genetic Programming estimation of distribution algorithms Probabilistic Model-Building Denoising Autoencoders

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SDR: A Better Trigger for Adaptive Variance Scaling in Normal EDAs 07

SDR: A Better Trigger for Adaptive Variance Scaling in Norma...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Bosman, Peter A. N. Grahl, Joern Rothlauf, Franz Ctr Math & Comp Sci POB 94079 NL-1090 GB Amsterdam Netherlands Univ Mannheim Dept Logist D-68131 Mannheim Germany Univ Mannheim Dept Informat Syst D-68163 Mannheim Germany

ISBN: (纸本)9781595936974

Recently, advances have been made in continuous, normal-distribution-based estimation-of-distribution (EDAs) by scaling the variance up from the maximum-likelihood estimate. When done properly, such scaling has been shown to prevent premature convergence on slope-like regions of the search space. In this paper we specifically focus on one way of scaling that was previously introduced as Adaptive Variance Scaling (ANTS.). It was found that when using ANTS, the average number of fitness evaluations grows subquadratically with the dimensionality on a wide range of unimodal test-problems, competitively with the CMA-ES. Still, room for improvement exists because the. variance doesn't always have to be scaled. A previously introduced trigger based oil correlation that determines when to apply scaling was shown to fail on higher dimensional problems. Here we provide a new solution called the Standard-Deviation Ratio (SDR.) trigger that is integrated with the Iterated Density-estimation Evolutionary Algorithm (IDEA). Intuitively put, scaling is triggered with SDR only if improvements are found to be far away from the mean. SDR, works even in high dimensions as a result of factorizing the decision rule behind the trigger according to the estimated Bayesian factorization. We evaluate SDR-AVS-IDEA on the same set of benchmark problems and compare it with ANTS-IDEA and CMA-ES. We find that the addition of SDR gives AVS-IDEA an important extra edge for it to be used in future research and in applications both in single-objective optimization as well as in multi-objective and dynamic optimization In addition, we provide practical rules of thumb for parameter settings for using SDR-AVS-IDEA that, result in an asymptotic scale-up behavior that is sublinear for the population size (O(l(0.85))) and subquadratic (O(l(1.85))) for the number of evaluations.

关键词： Evolutionary algorithms estimation of distribution algorithms Numerical Optimization Adaptive Variance Scaling

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A comparative study of probability collectives based multi-agent systems and Genetic algorithms 05

A comparative study of probability collectives based multi-a...

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Genetic and Evolutionary Computation Conference

作者： Huang, Chien-Feng Bieniawski, Stefan Wolpert, David H. Strauss, Charlie E. M. Los Alamos Natl Lab Ctr Nonlinear Studies Los Alamos NM 87544 USA

ISBN: (纸本)1595930108

We compare Genetic algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems (MAS). It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.

关键词： multi-agent systems Probability Collectives Product distribution theory genetic algorithms estimation of distribution algorithms

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An Evolutionary Algorithm Based Hyper-heuristic for the Job-Shop Scheduling Problem with No-Wait Constraint 4th

An Evolutionary Algorithm Based Hyper-heuristic for the Job-...

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Chaurasia, Sachchida Nand Sundar, Shyam Jung, Donghwi Lee, Ho Min Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 136713 South Korea Natl Inst Technol Dept Comp Applicat Raipur Madhya Pradesh India Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea

ISBN: (纸本)9789811307614;9789811307607

In this paper, we developed an evolutionary algorithm with guided mutation (EA/G) based hyper-heuristic for solving the job-shop scheduling problem with no-wait constraint (JSPNW). The JSPNW is an extension of well-known job-shop scheduling problem subject to the constraint that no waiting time is allowed between operations for a given job. This problem is a typical NP-hard problem. The hyper-heuristic algorithm comprises of two level frameworks. In the high-level, an evolutionary algorithm is employed to explore the search space. The low-level, which is comprised of generic as well as problem-specific heuristics such as guided mutation, multi-insert points and multi-swap. EA/G is a recent addition to the class of evolutionary algorithm that can be considered as a hybridization of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs), and which tries to overcome the shortcomings of both. In GAs, the location information of the solutions found so far is directly used to generate offspring. On the other hand, EDAs use global statistical information to generate new offspring. In EDAs the global statistical information is stored in the form probability vector, and a new offspring is generated by sampling this probability vector. We have compared our approach with the state-of-the-art approaches. The computational results show the effectiveness of our approach.

关键词： Scheduling Job-shop No-wait Genetic algorithms Constrained optimization estimation of distribution algorithms Guided mutation Heuristic Hyper-heuristic

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