Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. Univari...
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ISBN:
(纸本)9783037853122
Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. univariate marginal distribution algorithm is a modified Evolutionary algorithms, which has some advantages over classical Evolutionary algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of univariate marginal distribution algorithm. The experimental results showed that the proposed method could effectively improve the accuracy of classifier.
A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While ...
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A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While the optimal maximum likelihood (ML) decoder using an exhaustive search method is prohibitively complex, simulation results show that the CA and UMDA optimized MIMO detection algorithms result in near optimal bit error rate (BER) performance with significantly reduced computational complexity. The results also suggest that the heuristic based MIMO detection outperforms the vertical bell labs layered space time (VBLAST) detector without severely increasing the detection complexity. The performance of UMDA is found to be superior to that of CA in terms of computational complexity and the BER performance. (C) 2009 Elsevier Ltd. All rights reserved.
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.
Feature subset selection for unsupervised learning, is a very important topic in artificial intelligence because it is the base for saving computational resources. In this implementation we use a typical testor's ...
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ISBN:
(纸本)9783642052576
Feature subset selection for unsupervised learning, is a very important topic in artificial intelligence because it is the base for saving computational resources. In this implementation we use a typical testor's methodology in order to incorporate an importance index for each variable. This paper presents the general framework and the way two hybridized meta-heuristics work in this NP-complete problem. The evolutionary mechanisms are based on the univariate marginal distribution algorithm (UMDA) and the Genetic algorithm (GA). GA and UMDA Estimation of distributionalgorithm (EDA) use a very useful rapid operator implemented for finding typical testors on a very large dataset and also, both algorithms, have a local search mechanism for improving time and fitness. Experiments show that EDA is faster than GA because it has a better exploitation performance;nevertheless. GA' solutions are more consistent.
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
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.
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.
This paper introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple...
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This paper introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of the GA's parameters and operators. The paper clearly illustrates the mapping of the simple GA's parameters into those of an equivalent compact GA;Computer simulations compare both algorithms in terms of solution quality and speed. Finally, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GA's.
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