Dengue Virus (DENV) infection is one of the rapidly spreading mosquito-borne viral infections in humans. Every year, around 50 million people get affected by DENV infection, resulting in 20,000 deaths. Despite the rec...
详细信息
Dengue Virus (DENV) infection is one of the rapidly spreading mosquito-borne viral infections in humans. Every year, around 50 million people get affected by DENV infection, resulting in 20,000 deaths. Despite the recent experiments focusing on dengue infection to understand its functionality in the human body, several functionally important DENV-human protein-protein interactions (PPIs) have remained unrecognized. This article presents a model for predicting new DENV-human PPIs by combining different sequence-based features of human and dengue proteins like the amino acid composition, dipeptide composition, conjoint triad, pseudo amino acid composition, and pairwise sequence similarity between dengue and human proteins. A Learning vector quantization (LVQ)-based compact genetic algorithm (CGA) model is proposed for feature subset selection. CGA is a probabilistic technique that simulates the behavior of a geneticalgorithm (GA) with lesser memory and time requirements. Prediction of DENV-human PPIs is performed by the weighted Random Forest (RF) technique as it is found to perform better than other classifiers. We have predicted 1013 PPIs between 335 human proteins and 10 dengue proteins. All predicted interactions are validated by literature filtering, GO-based assessment, and KEGG Pathway enrichment analysis. This study will encourage the identification of potential targets for more effective anti-dengue drug discovery.
Estimation of distribution algorithms (EDAs) are general-purpose optimizers that maintain a probability distribution over a given search space. This probability distribution is updated through sampling from the distri...
详细信息
Estimation of distribution algorithms (EDAs) are general-purpose optimizers that maintain a probability distribution over a given search space. This probability distribution is updated through sampling from the distribution and a reinforcement learning process which rewards solution components that have shown to be part of good quality samples. The compact genetic algorithm (cGA) is a non-elitist EDA able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. We investigate the cGA on the Cliff function for which it was shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the Cliff function and investigate its dynamics both experimentally and theoretically. Our experimental results indicate that the cGA requires exponential time for all values of the update strength 1/K. We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for K where the expected optimization time drops from n Theta(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n<^>{\Theta (n)}$$\end{document} to 2 Theta(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2<^>{\Theta (n)}$$\end{document}.
An ontology is a state-of-the-art knowledge modeling technique in the natural language domain, which has been widely used to overcome the linguistic barriers in Asian and European countries' intelligent applicatio...
详细信息
An ontology is a state-of-the-art knowledge modeling technique in the natural language domain, which has been widely used to overcome the linguistic barriers in Asian and European countries' intelligent applications. However, due to the different knowledge backgrounds of ontology developers, the entities in the ontologies could be defined in different ways, which hamper the communications among the intelligent applications built on them. How to find the semantic relationships among the entities that are lexicalized in different languages is called the Cross-lingual Ontology Matching problem (COM), which is a challenge problem in the ontology matching domain. To face this challenge, being inspired by the success of the geneticalgorithm (GA) in the ontology matching domain, this work proposes a compact GA with Annealing Re-sample Inheritance mechanism (CGA-ARI) to efficiently address theCOMproblem. In particular, a Cross-lingual Similarity Metric (CSM) is presented to distinguish two cross-lingual entities, a discrete optimal model is built to define the COM problem, and the compact encoding mechanism and the Annealing Re-sample Inheritance mechanism (ARI) are introduced to improve CGA's searching performance. The experiment uses Multifarm track to test CGA-ARI's performance, which includes 45 ontology pairs in different languages. The experimental results show that CGA-ARI is able to significantly improve the performance of GA and CGA and determine better alignments than state-of-the-art ontology matching systems.
The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. I...
详细信息
ISBN:
(纸本)9781450392372
The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the Cliff function for which it has been shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the Cliff function and investigate its dynamics both experimentally and theoretically around the Cliff. Our experimental results indicate that the cGA requires exponential time for all values of the update strength... We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for.. where the expected optimization time drops from n Theta((n)) to 2 Theta((n)).
The compact genetic algorithm (cGA) evolves a probability distribution favoring optimal solutions in the underlying search space by repeatedly sampling from the distribution and updating it according to promising samp...
详细信息
The compact genetic algorithm (cGA) evolves a probability distribution favoring optimal solutions in the underlying search space by repeatedly sampling from the distribution and updating it according to promising samples. We study the intricate dynamics of the cGA on the test function OneMax, and how its performance depends on the hypothetical population size K, which determines how quickly decisions about promising bit values are fixated in the probabilistic model. It is known that the cGA and the Univariate Marginal Distribution algorithm (UMDA), a related algorithm whose population size is called lambda, run in expected time O(nlogn) when the population size is just large enough (K=Theta(nlogn) and lambda=Theta(nlogn) , respectively) to avoid wrong decisions being fixated. The UMDA also shows the same performance in a very different regime (lambda=Theta(logn), equivalent to K=Theta(logn) in the cGA) with much smaller population size, but for very different reasons: many wrong decisions are fixated initially, but then reverted efficiently. If the population size is even smaller (o(logn)), the time is exponential. We show that population sizes in between the two optimal regimes are worse as they yield larger runtimes: we prove a lower bound of omega(K1/3n+nlogn) for the cGA on OneMax for K=O(n/log2n). For K=omega(log3n) the runtime increases with growing K before dropping again to O(Kn+nlogn) for K=omega(nlogn) . This suggests that the expected runtime for the cGA is a bimodal function in K with two very different optimal regions and worse performance in between.
Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automa...
详细信息
Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment's quality cannot be ensured. To overcome this drawback, in this work, an Interactive compact genetic algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm's performance and the alignment's quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert's workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)'s benchmark to test our proposal's performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment's quality, and it also outperforms OAEI's participants.
This paper presents a solver, the PIMAGc, for optimization of constrained mixed integer programming problems based on the compact genetic algorithm (cGA). As the cGA, the PIMAGc works with binary representation of var...
详细信息
This paper presents a solver, the PIMAGc, for optimization of constrained mixed integer programming problems based on the compact genetic algorithm (cGA). As the cGA, the PIMAGc works with binary representation of variables. As comparison criteria and to measure the algorithm's performance, the PIMAGc was compared with the MI-LXPM, an appropriate algorithm for solving mixed integer programming problems. By using an appropriate number of problems, it was found that the PIMAGc outperformed the MI-LXPM with a greater success rate and a smaller number of evaluations on the majority of the problems, with equal performance on the others.
We study the intricate dynamics of the compact genetic algorithm (cGA) on ONEMAX, and how its performance depends on the step size 1/K, that determines how quickly decisions about promising bit values are fixed in the...
详细信息
ISBN:
(纸本)9781450356183
We study the intricate dynamics of the compact genetic algorithm (cGA) on ONEMAX, and how its performance depends on the step size 1/K, that determines how quickly decisions about promising bit values are fixed in the probabilistic model. It is known that cGA and UMDA, a related algorithm, run in expected time O(n log n) when the step size is just small enough (K = Theta(root n log n) to avoid wrong decisions being fixed. UMDA also shows the same performance in a very different regime (equivalent to K = Theta(log n) in the cGA) with much larger steps sizes, but for very different reasons: many wrong decisions are fixed initially, but then reverted efficiently. We show that step sizes in between these two optimal regimes are harmful as they yield larger runtimes: we prove a lower bound Omega(K(1/3)n + n log n) for the cGA on ONEMAX for K = O(root n/log(2) n). For K = Omega(log(3) n) the runtime increases with growing K before dropping again to O(K root n + nlogn) for K = Omega(root n log n). This suggests that the expected runtime for cGA is a bimodal function in K with two very different optimal regions and worse performance in between.
In this letter, a new optimization algorithm, the Modified compact genetic algorithm (M-cGA) is introduced and applied to the synthesis of thinned arrays. The M-cGA has been derived from the compact genetic algorithm ...
详细信息
In this letter, a new optimization algorithm, the Modified compact genetic algorithm (M-cGA) is introduced and applied to the synthesis of thinned arrays. The M-cGA has been derived from the compact genetic algorithm (cGA), properly modified and improved by implementing more than one probability vector (PV) and adding suitable learning scheme between these PVs. The so-obtained algorithm has been applied to the optimized synthesis of different-size linear and planar thinned arrays: In all the considered cases, it outperforms not only the cGA, but also the other optimization schemes previously applied to this kind of problem, both in terms of goodness of the solution (minimization of the peak sidelobe level) and of computational cost.
The compact genetic algorithm decoder has been introduced in [9] as an efficient decoding method of linear block codes. It requires less storage memory than geneticalgorithms based decoders. One of its major weakness...
详细信息
ISBN:
(纸本)9781509062270
The compact genetic algorithm decoder has been introduced in [9] as an efficient decoding method of linear block codes. It requires less storage memory than geneticalgorithms based decoders. One of its major weakness is the big number of necessary iterations to reach convergence in comparison with geneticalgorithms (GA) based decoders. We propose, in this work, new ideas allowing us to reduce the number of iterations from about 10(5) to just about 10(3) which reduces the complexity of decoding. This, without decreasing the decoding performance. We introduce a new stopping criterion based on the soft weight of the probability vector p, a new initialization method of p and we tried to combine both methods all together. Both performance study and the calculation of the average number of iterations ensure the effectiveness of the proposed decoder.
暂无评论