Theory and Practice of Cellular UMDA for Discrete Optimization

Alba, Enrique; Madera, Julio; Dorronsoro, Bernabè; Ochoa, Alberto; Soto, Marta

doi:10.1007/11844297_25

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…Empty cells are allowed (Kirley, 2002) [147] CGAD: cGA with disasters (Alba & Dorronsoro, 2003) [10,12] Proposal of cGAs with adaptive population (Li & Sutherland, 2002) [165] Prey/predator algorithm for continuous optimization [100] Takeover in asynchronous cGAs with ring population [99] Selection pressure study in cGAs with ring population (Li, 2003) [164] Prey/predator algorithm for the multi-objective domain [9] Comparison between cGAs and other EAs [11] Some hybrid cGAs for VRP [17,74] Comparison between synchronous and asynchronous cGAs [98] Selection pressure in asynchronous cGAs with toroidal pop. [7] Asynchronous cGAs with adaptive populations [8,18] cMOGA: first orthodox multi-objective cGA [15] First memetic cGA (cMA); applied on SAT (Alba & Saucedo, 2005) [24] Comparison between cGA and panmictic GAs (Dick, 2005) [68] A cGA with ring population as a method for preserving niches (Dick, 2005) [69] Modelling the genetic evolution in cGAs (ring population) [102] Modelling cGAs with square and rectangular populations [101] Modelling cGAs with small world topology populations [13] Hybrid cGA which improves the state of art in VRP [23] First EDA with population structured in a cellular way [73] A cGA for the numerical optimization domain (Grimme & Schmitt, 2006) [125] Prey/predator algorithm for multi-objective domain (Ishibuchi et al, 2006) [138] A cGA with distinct neighborhoods for selection and crossover (Luo & Liu, 2006) …”

Section: The Research In the Theory Of The Cellular Modelsmentioning

confidence: 99%

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Cellular Genetic Algorithms

Alba

2008

Operations Research/Computer Science Interfaces Series

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Section: The Research In the Theory Of The Cellular Modelsmentioning

confidence: 99%

“…3 for a deep comparison in the field of GAs, and Chaps. 10 and 13 for the cases of EDAs [23] and memetic GAs [14], respectively). …”

Section: Empirical Studies On the Behavior Of Ceasmentioning

confidence: 99%

Cellular Genetic Algorithms

Alba

2008

Operations Research/Computer Science Interfaces Series

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“…Although traditional Evolution Algorithms (EAs), such as GA, PSO and ACO, are an effective way to solve feature selection problem [10], traditional EAs have some drawbacks: firstly, many parameters need to be tuned; secondly, they are easy to fall in local optimal solution; thirdly, computation time accumulates exponentially as population size. In order to overcome these shortcomings, a new branch of EAs, namely Estimation of Distribution Algorithms (EDAs) [11], was employed in this paper to solve the feature selection problem.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection on the Basis of Rough Set Theory and Univariate Marginal Distribution Algorithm

Wei¹,

Zhang²,

Liu³

et al. 2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-Feature selection is an important preprocessing step in machine learning. The aim of feature selection is to find an optimal subset from original features that satisfies a criterion. Rough set theory (RST) is one of the most effective ways to solve feature selection problem, but RST is inefficient in large scale datasets. In order to solve this problem, in this paper, we proposed a novel feature selection algorithm RSUMDA on the basis of univariate marginal distribution algorithm. RST was used to obtain the significance of each feature as the original probability of UMDA and then UMDA was to search the optimal feature subset that using the number of the selected feature and the accuracy of the classifier as fitness function. Experimentation was carried out in 4 UCI datasets. The results showed that our algorithm could effectively reduce the number of the features, improve the accuracy of the classifier and quicken the convergence rate.

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“…However, the contribution of 0 and 1 to the disease may be different. Therefore, the distribution information (DI) is added to (7). …”

Section: Ms_fdcc_di Encodingmentioning

confidence: 99%

“…Thus, univariate marginal distribution algorithm (UMDA) is used in this paper and the support vector machine (SVM) is adopted as the evaluator. However, in UMDA, lack of diversity is the dominant factor converging to local optimum solutions [7]. Therefore, a novel dynamic elite selection strategy is proposed to overcome this problem.…”

Section: Introductionmentioning

confidence: 99%

Identification of Disease-Associated Combination of SNPs Using a Hybrid Algorithm with Multiple Encoding Approaches

Zhao¹,

Wei²

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-Background: Individual SNP often only exhibit a small effect, but combinations of SNPs are assumed to be strongly influence the risk of disease. Obviously, selecting an optimal subset of SNPs, which most associated with disease, is a NP-hard problem. Results: To obtain a higher performance of predicting power for disease status and a higher computing efficiency, we proposed a double-filter-wrapper (DFW) algorithm to identify the optimal subset of SNPs. Moreover, few studies have been carried out to solve the SNPs encoding issues. On the basis of the differences of statistical properties between case and control, three types of encoding methods were proposed to generate the input for the DFW. Conclusion: We used five complex disease datasets to verify the effectiveness of our algorithm. The experimental results showed that our method appears more promising than other current methods for identifying the associated SNPs. In addition, the results also indicate that the encoding method proposed in this paper can much more accurately reflect the real situation.

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Theory and Practice of Cellular UMDA for Discrete Optimization

Cited by 11 publications

References 6 publications

Cellular Genetic Algorithms

Cellular Genetic Algorithms

Feature Selection on the Basis of Rough Set Theory and Univariate Marginal Distribution Algorithm

Identification of Disease-Associated Combination of SNPs Using a Hybrid Algorithm with Multiple Encoding Approaches

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