Efficiency enhancement techniques - such as parallelization and hybridization - are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research a...
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Efficiency enhancement techniques - such as parallelization and hybridization - are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of Theta(n(0.26)) to Theta(n(0.5)) where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence;nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structure-building period, which is the only parameter of the proposed sporadic model-building approach. The paper then tests the proposed method and the rule for setting the structure-building period on the problem of finding ground states of 2D and 3D Ising spin glasses.
Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the m...
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Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.
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 ...
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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.
The hierarchical bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of pr...
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ISBN:
(纸本)9781595936974
The hierarchical bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.
Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to ...
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Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.
Estimation of Distribution algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used to learn probabilistic models of the se...
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ISBN:
(纸本)9781595931863
Estimation of Distribution algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used to learn probabilistic models of the selected population. This model is then used to generate next population via sampling. An important phenomenon in machine learning from data is called overfitting. This occurs when the model is overly adapted to the specifics of the training data so well that even noise is encoded. The purpose of this paper is to investigate. whether overfitting happens in EDAs, and to discover its consequences. What is found is: overfitting does occur in EDAs;overfitting correlates to EDAs performance;reduction of overfitting using early stopping can improve EDAs performance.
This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical bayesian optimization algorithm (hBOA) and other advanced estimation of distribution algorithm...
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ISBN:
(纸本)9781595931863
This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical bayesian optimization algorithm (hBOA) and other advanced estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once every few iterations (generations), whereas in the remaining iterations only model parameters (conditional and marginal probabilities) axe updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup that decreases the asymptotic time complexity of model building in hBOA by a factor of Theta(n(0.26)) to Theta(n(0.5)), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence;nonetheless, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building.
To solve a wide range of different problems, the research in black-box optimization faces several important challenges. One of the most important challenges is the design of methods capable of automatic discovery and ...
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To solve a wide range of different problems, the research in black-box optimization faces several important challenges. One of the most important challenges is the design of methods capable of automatic discovery and exploitation of problem regularities to ensure efficient and reliable search for the optimum. This paper discusses the bayesian optimization algorithm (BOA), which uses bayesian networks to model promising solutions and sample new candidate solutions. Using bayesian networks in combination with population-based genetic and evolutionary search allows BOA to discover and exploit regularities in the form of a problem decomposition. The paper analyzes the applicability of the methods for learning bayesian networks in the context of genetic and evolutionary search and concludes that the combination of the two approaches yields robust, efficient, and accurate search. (C) 2002 Elsevier Science Inc. All rights reserved.
This paper applies the bayesian optimization algorithm with Tabu Search (Tabu-BOA) to electric equipment configuration problems in a power plant. Tabu-BOA is a hybrid evolutionary computation algorithm with competent ...
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This paper applies the bayesian optimization algorithm with Tabu Search (Tabu-BOA) to electric equipment configuration problems in a power plant. Tabu-BOA is a hybrid evolutionary computation algorithm with competent genetic algorithms and meta-heuristics. The configuration problems we consider have complex combinatorial properties, and therefore are hard to formulate and solve via conventional mathematical programming techniques. Using the proposed method, we have solved the following problems (in order of increasing complexity): (I) cost minimization of electric equipment configuration and the corresponding cabling, (2) plus choice of the power plant operation patterns, (3) plus parallel operation of multiple transformers, (4) plus change of the supply voltages (high voltage or low voltage) to the electric power load, and (5) with addition of another objective, that is, both minimization of the cost and maximization of the surplus power supply. (C) 2005 Wiley Periodicals, Inc.
This paper describes an evolutionary algorithm for optimization of continuous problems that combines advanced recombination techniques for discrete representations with advanced mutation techniques for continuous repr...
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This paper describes an evolutionary algorithm for optimization of continuous problems that combines advanced recombination techniques for discrete representations with advanced mutation techniques for continuous representations. Discretization is used to transform solutions between the discrete and continuous domains. The proposed algorithm combines the strengths of purely continuous and purely discrete approaches and eliminates some of their disadvantages. The paper tests the proposed algorithm with the recombination operator of the bayesian optimization algorithm, sigma-self-adaptive mutation, and three discretization methods. The empirical results on three problems suggest that the tested variant of the algorithm scales up well on all tested problems, indicating good scalability over a broad range of continuous problems. (C) 2003 Elsevier Inc. All rights reserved.
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