Background and objective: Clustering is a widely used popular method for data analysis within many clustering algorithms for years. Today it is used in many predictions, collaborative filtering and automatic segmentat...
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Background and objective: Clustering is a widely used popular method for data analysis within many clustering algorithms for years. Today it is used in many predictions, collaborative filtering and automatic segmentation systems on different domains. Also, to be broadly used in practice, such clustering algorithms need to give both better performance and robustness when compared to the ones currently used. In recent years, evolutionary algorithms are used in many domains since they are robust and easy to implement. And many clustering problems can be easily solved with such algorithms if the problem is modeled as an optimization problem. In this paper, we present an optimization approach for clustering by using four well-known evolutionary algorithms which are Biogeography-Based Optimization (BBO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Method: the objective function has been specified to minimize the total distance from cluster centers to the data points. Euclidean distance is used for distance calculation. We have applied this objective function to the given algorithms both to find the most efficient clustering algorithm and to compare the clustering performances of algorithms against different data sizes. In order to benchmark the clustering performances of algorithms in the experiments, we have used a number of datasets with different data sizes such as some small scale, medium and big data. The clustering performances have been compared to K-means as it is a widely used clustering algorithm for years in literature. Rand Index, Adjusted Rand Index, Mirkin's Index and Hubert's Index have been considered as parameters for evaluating the clustering performances. Result: As a result of the clustering experiments of algorithms over different datasets with varying data sizes according to the specified performance criteria, GA and GWO algorithms show better clustering performances among the others. Conclusions: The r
Surrogate-assisted evolutionary algorithms (SAEAs) have attracted considerable attention for reducing the computation time required by an EA on computationally expensive optimization problems. In such algorithms, a su...
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In this work, we employed ab initio methods combined with evolutionary algorithms for searching stable structures for fluorine in the terapascal (TPa) regime. We performed several structural searches using the USPEX c...
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This paper presents theoretical studies on Asymptotic Convergence Rate (ACR) for finite dimensional optimization. Given the problem function (fitness function), ACR measures how fast an iterative optimization method c...
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Interactive dynamic influence diagrams~(I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into...
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ISBN:
(纸本)9781450392136
Interactive dynamic influence diagrams~(I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms.
Theoretical studies on evolutionary algorithms have developed vigorously in recent years. Many such algorithms have theoretical guarantees in both running time and approximation ratio. Some approximation mechanism see...
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One of the main problems of evolutionary algorithms is the convergence of the population to local minima. In this paper, we explore techniques that can avoid this problem by encouraging a diverse behavior of the agent...
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Optimizing gait stability for legged robots is a difficult problem. Even on level surfaces, effectively traversing across different textures (e.g., carpet) rests on dynamically tuning parameters in multidimensional sp...
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While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve per...
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Scramjet engines are a hypersonic airbreathing technology that offers a potential for economical and flexible space transportation in lieu of traditional rocket-based systems. Accurate prediction of inviscid flowfield...
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Scramjet engines are a hypersonic airbreathing technology that offers a potential for economical and flexible space transportation in lieu of traditional rocket-based systems. Accurate prediction of inviscid flowfields is of particular importance for high-performance intake design, prior to consideration of viscous effects in the design process. Further, inviscid axisymmetric intakes serve as a base for streamline tracing, one of the most promising design methodologies for scramjet intakes. Multi-objective optimization studies have been conducted via surrogate-assisted evolutionary algorithm to gain physical insights into axisymmetric intake design in this study. The results indicate the existence of global optimum solutions that can simultaneously achieve maximum compression efficiency and minimum drag for any degree of compression in case the outflow is supersonic at the intake exit, which has been verified by theory. In addition, a correlation between compression efficiency and flow uniformity has been found and discussed quantitatively. This assures the optimality of the Busemann intakes in that they simultaneously offer high compression efficiency and uniform flow at the intake exit in the inviscid regime. (C) 2021 Elsevier Masson SAS. All rights reserved.
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