For discrete optimization, the two basic search principles prevailing are stochastic local search and population based search. Local search has difficulties to get out of local optima. Here variable neighborhood searc...
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ISBN:
(纸本)0780372824
For discrete optimization, the two basic search principles prevailing are stochastic local search and population based search. Local search has difficulties to get out of local optima. Here variable neighborhood search outperforms stochastic local search methods which accept worse points with a certain probability. Population based search performs best on problems with sharp gaps. It is outperformed by stochastic local search only when there are many paths to good local optima.
Frustrated Ising spin glasses represent a rich class of challenging optimization problems that share many features with other complex, highly multimodal optimization and combinatorial problems. This paper shows that t...
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ISBN:
(纸本)9781595936974
Frustrated Ising spin glasses represent a rich class of challenging optimization problems that share many features with other complex, highly multimodal optimization and combinatorial problems. This paper shows that transforming candidate solutions to an alternative representation that is strongly tied to the energy function simplifies the exploration of the space of potential spin configurations and that it significantly improves performance of evolutionary algorithms with simple variation operators on Ising spin glasses. The proposed techniques are incorporated into the simple genetic algorithm, the univariate marginal distribution algorithm, and the hierarchical Bayesian optimization algorithm.
This paper presents the comparison of two different algorithms: a univariate marginal distribution algorithm for Analog Circuits (UMDA-AC) and a Genetic algorithm for Analog Circuits (GA-AC). These algorithms are comp...
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ISBN:
(纸本)9780769539331
This paper presents the comparison of two different algorithms: a univariate marginal distribution algorithm for Analog Circuits (UMDA-AC) and a Genetic algorithm for Analog Circuits (GA-AC). These algorithms are compared in performing the synthesis of topology and sizing of an analog low pass filter. Modeling of circuits is made by means of a linear representation technique with a variable length chromosome. Evaluation of circuits' functionality is carried out by Simulation Program with Integrated Circuits Emphasis (Spice), since one of the objectives is to keep as low as possible the amount of non Spice-Simulable circuits while keep elements' values within preferred ones. Experiments show the effectiveness of a set of evolvable mechanisms in both algorithms, and while GA-AC and its three genetic operators are more able to keep low the rate of non Spice-Simulable circuits;UMDA-AC performs less evaluations by means of its estimated distribution
With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. ...
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ISBN:
(纸本)9781424447138
With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. One main challenge for this study is to find the optimal subsets of Single Nucleotide Polymorphisms (SNPs) which are most tightly associated with diseases. Feature selection has become a necessity in many bioinformatics applications. In this paper, we propose a wrapper algorithm named USVM which combines univariate marginal distribution algorithm (UMDA) and Support Vector Machine (SVM) for disease association study. USVM not only eliminates the redundancy of feature, but also solves the problem of SVM's parameters selection. We use USVM to analyze the Crohn's disease (CD) dataset including 387 samples and each one has 103 SNPs. The experimental results show that our algorithm outperforms the current algorithms including DNF, CSP, ORF and so on.
With apparently all research on estimation-of-distributionalgorithms (EDAs) concentrated on pseudo-Boolean optimization and permutation problems, we undertake the first steps towards using EDAs for problems in which ...
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ISBN:
(纸本)9798400701191
With apparently all research on estimation-of-distributionalgorithms (EDAs) concentrated on pseudo-Boolean optimization and permutation problems, we undertake the first steps towards using EDAs for problems in which the decision variables can take more than two values, but which are not permutation problems. To this aim, we propose a natural way to extend the known univariate EDAs to such variables. Different from a naive reduction to the binary case, it avoids additional constraints. Since understanding genetic drift is crucial for an optimal parameter choice, we extend the known quantitative analysis of genetic drift to EDAs for multi-valued variables. Roughly speaking, when the variables take r different values, the time for genetic drift to become critical is r times shorter than in the binary case. Consequently, the update strength of the probabilistic model has to be chosen r times lower now. To investigate how desired model updates take place in this framework, we undertake a mathematical runtime analysis on the.. -valued LeadingOnes problem. We prove that with the right parameters, the multi-valued UMDA solves this problem efficiently in O (r log(r)(2) n(2) log(n)) function evaluations. Overall, our work shows that EDAs can be adjusted to multi-valued problems and gives advice on how to set their parameters.
The paper is focused on evolutionary synthesis of analog circuit realization of cube root function using proposed Graph Hybrid Estimation of distributionalgorithm. The problem of cube root function circuit realizatio...
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The paper is focused on evolutionary synthesis of analog circuit realization of cube root function using proposed Graph Hybrid Estimation of distributionalgorithm. The problem of cube root function circuit realization was adopted to demonstrate synthesis capability of the proposed method. Individuals of the population of the proposed method which represent promising topologies are encoded using graphs and hypergraphs. Hybridization with local search algorithm was used. The proposed method employs univariate probabilistic model.
The majority of research on estimation -of -distributionalgorithms (EDAs) concentrates on pseudoBoolean optimization and permutation problems, leaving the domain of EDAs for problems in which the decision variables c...
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The majority of research on estimation -of -distributionalgorithms (EDAs) concentrates on pseudoBoolean optimization and permutation problems, leaving the domain of EDAs for problems in which the decision variables can take more than two values, but which are not permutation problems, mostly unexplored. To render this domain more accessible, we propose a natural way to extend the known univariate EDAs to this setting. Different from a na & iuml;ve reduction to the binary case, our approach avoids additional constraints. Since understanding genetic drift is crucial for an optimal parameter choice, we extend the known quantitative analysis of genetic drift to EDAs for multi -valued, categorical variables. Roughly speaking, when the variables take r different values, the time for genetic drift to become significant is r times shorter than in the binary case. Consequently, the update strength of the probabilistic model has to be chosen r times lower now. To investigate how desired model updates take place in this framework, we undertake a mathematical runtime analysis on the r -valued L EADING O NES problem. We prove that with the right parameters, the multi -valued UMDA solves this problem efficiently in O ( r ln( r ) 2 n 2 ln( n )) function evaluations. This bound is nearly tight as our lower bound Omega( r ln( r ) n 2 ln( n )) shows. Overall, our work shows that our good understanding of binary EDAs naturally extends to the multi -valued setting, and it gives advice on how to set the main parameters of multi -values EDAs.
Most of the human genetic variations are single nucleotide polymorphisms(SNPs),and among them,non-synonymous SNPs,also known as SAPs,attract extensive *** can be neural or disease *** studies have been done to disting...
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Most of the human genetic variations are single nucleotide polymorphisms(SNPs),and among them,non-synonymous SNPs,also known as SAPs,attract extensive *** can be neural or disease *** studies have been done to distinguish deleterious SAPs from neutral *** many previous studies were based on both structural and sequence features of the SAP,these methods are not applicable when protein structures are not *** the current paper,we developed a method based on UMDA and SVM using protein sequence information to predict SAP's disease *** extracted a set of features that are independent of protein structure for each *** a SVM-based machine-learning classifier that used grid search to tune parameters was applied to predict the possible disease association of *** SVM method reaches good prediction *** the input data of SVM contain irrelevant and noisy features and parameters of SVM also affect the prediction performance,we introduced UMDA-based wrapper approach to search for the 'best' *** UMDA-based method greatly improved prediction *** with current method,our method achieved better performance.
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