This paper presents a novel and promising approach to turbulence model formulation, rather than putting forward a particular new model. evolutionary computation has brought symbolic regression of scalar fields into th...
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This paper presents a novel and promising approach to turbulence model formulation, rather than putting forward a particular new model. evolutionary computation has brought symbolic regression of scalar fields into the domain of algorithms and this paper describes a novel expansion of Gene Expression Programming for the purpose of tensor modeling. By utilizing high-fidelity data and uncertainty measures, mathematical models for tensors are created. The philosophy behind the framework is to give freedom to the algorithm to produce a constraint-free model;its own functional form that was not previously imposed. Turbulence modeling is the target application, specifically the improvement of separated flow prediction. Models are created by considering the anisotropy of the turbulent stress tensor and formulating non-linear constitutive stress-strain relationships. A previously unseen flow field is computed and compared to the baseline linear model and an established non-linear model of comparable complexity. The results are highly encouraging. (C) 2016 Elsevier Inc. All rights reserved.
The function of operators in an evolutionary algorithm (EA) is very crucial as the operators have a strong effect on the performance of the EA. In this paper, a new selection operator is introduced for a real valued e...
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The function of operators in an evolutionary algorithm (EA) is very crucial as the operators have a strong effect on the performance of the EA. In this paper, a new selection operator is introduced for a real valued encoding problem, which specifically exists in a shrimp diet formulation problem. This newly developed selection operator is a hybrid between two well-known established selection operators: roulette wheel and binary tournament selection. A comparison of the performance of the proposed operator and the other existing operator was made for evaluation purposes. The result shows that the proposed roulette-tournament selection is better in terms of its ability to provide many good feasible solutions when a population size of 30 is used. Thus, the proposed roulette-tournament is suitable and comparable to established selection for solving a real valued shrimp diet formulation problem. The selection operator can also be generalized to any problems related to EA.
Typically the design of a Radio-Frequency (RF) circuit is difficult, time-consuming and often based around an iterative process. In this manuscript, an automatic synthesis of three typical blocks of nowadays RF front-...
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Typically the design of a Radio-Frequency (RF) circuit is difficult, time-consuming and often based around an iterative process. In this manuscript, an automatic synthesis of three typical blocks of nowadays RF front-end receivers, a narrowband differential low-noise amplifier, a mixer and a local oscillator, is presented. The synthesis of the three circuits was made at sizing level and was carried out by Analog IC Design Automation (AIDA). AIDA is a multi-objective multi-constraint simulator based automatic It design tool, which optimizes analog circuits through the usage of evolutionary computation. The performance potential of the circuits and tool is evaluated through electrical simulation results, which are finally compared with recently published state-of-the-art works, with overall better results and little time-consumption, proving the surplus value of using an automatic IC design tool in RF circuitry synthesis. (C) 2015 Elsevier B.V. All rights reserved.
All optical systems are to some extent burdened by one or more aberrations. Barrel distortion of an image is also an aberration. In this paper we used an innovative method to solve the problem of the centric radial di...
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All optical systems are to some extent burdened by one or more aberrations. Barrel distortion of an image is also an aberration. In this paper we used an innovative method to solve the problem of the centric radial distortion of a static image which serves for biometric identification of persons using 2D contour of a human hand. The method proposed uses a cascaded arrangement of two algorithms - the classic meta-heuristic, referred to as "jDE-differential evolution" and an algorithm called Covariance Matrix Adaptation Evolution Strategy. Optimizers use methods of inverse engineering and numerical mathematics to resolve the question of how to determine the correct parameters of the algebraic polynomial equation of the nth degree, by the application of which it is possible to obtain an image free of barrel distortion from an image affected by this distortion. The proposed method provides a high-quality and time-acceptable method of optimization and the option of choosing the approximation accuracy. With the use of the coefficients obtained, it is then possible to use a method called back-mapping to permanently correct the centric radial distortion aberration in the biometric scanner. Extensive experiments presented in this paper enable a better understanding of relationships, the accuracy obtained, and options of using evolutionary optimizers in a larger sense. (C) 2016 Elsevier Inc. All rights reserved.
For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are c...
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For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. And H-epsilon gate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved by Markov chain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.
To make the optimal design of the multilink transmission mechanism applied in mechanical press, the intelligent optimization techniques are explored in this paper. A preference polyhedron model and new domination rela...
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To make the optimal design of the multilink transmission mechanism applied in mechanical press, the intelligent optimization techniques are explored in this paper. A preference polyhedron model and new domination relationships evaluation methodology are proposed for the purpose of reaching balance among kinematic performance, dynamic performance, and other performances of the multilink transmission mechanism during the conceptual design phase. Based on the traditional evaluation index of single target of multicriteria design optimization, the robust metrics of the mechanism system and preference metrics of decision-maker are taken into consideration in this preference polyhedron model and reflected by geometrical characteristic of the model. At last, two optimized multilink transmission mechanisms are designed based on the proposed preference polyhedron model with different evolutionary algorithms, and the result verifies the validity of the proposed optimization method.
The paper presents an application of the in-house implementation of the evolutionary multi-objective algorithm. Different types of functionals, which depend on equivalent stress, displacement and total mass of the str...
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The paper presents an application of the in-house implementation of the evolutionary multi-objective algorithm. Different types of functionals, which depend on equivalent stress, displacement and total mass of the structure are defined. Values of the functionals are calculated on the basis of results obtained from numerical simulations. Numerical model of the UAV wing, composed of different laminate materials has been prepared and verified experimentally. Automatic calculation of the fitness functionals for the parameterized model is prepared. Examples of multi objective optimization by means of 2D and 3D Pareto-optimal set of solutions are presented. Effectiveness and usefulness of proposed method of multi-objective optimization are shown.
Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m - 1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-orga...
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Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m - 1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing mapping method with (m - 1) latent variables is applied to establish the neighborhood relationship among current solutions. A solution is only allowed to mate with its neighboring solutions to generate a new solution. To reduce the computational overhead, the self-organizing training step and the evolution step are conducted in an alternative manner. In other words, the self-organizing training is performed only one single step at each generation. The proposed algorithm has been applied to a number of test instances and compared with some state-of-the-art multiobjective evolutionary methods. The results have demonstrated its advantages over other approaches.
One of the most known and effective methods in supervised classification is the k-nearest neighbors classifier. Several approaches have been proposed to enhance its precision, with the fuzzy k-nearest neighbors (fuzzy...
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One of the most known and effective methods in supervised classification is the k-nearest neighbors classifier. Several approaches have been proposed to enhance its precision, with the fuzzy k-nearest neighbors (fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the representation of the relationship between the instances and the classes of the classification problems. Such a method would be very desirable, since it would potentially lead to an improvement in the precision of the classifier. In this work we present a new approach, evolutionary fuzzy k-nearest neighbors classifier using interval-valued fuzzy sets (EF-kNN-IVFS), incorporating interval-valued fuzzy sets for computing the memberships of training instances in fuzzy-kNN. It is based on the representation of multiple choices of two key parameters of fuzzy-kNN: one is applied in the definition of the membership function, and the other is used in the computation of the voting rule. Besides, evolutionary search techniques are incorporated to the model as a self-optimization procedure for setting up these parameters. An experimental study has been carried out to assess the capabilities of our approach. The study has been validated by using nonparametric statistical tests, and remarks the strong performance of EF-kNN-IVFS compared with several state of the art techniques in fuzzy nearest neighbor classification. (C) 2015 Elsevier Inc. All rights reserved.
Elucidating principles that underlie computation in neural networks is currently a major research topic of interest in neuroscience. Transfer Entropy (TE) is increasingly used as a tool to bridge the gap between netwo...
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ISBN:
(纸本)9781450349208
Elucidating principles that underlie computation in neural networks is currently a major research topic of interest in neuroscience. Transfer Entropy (TE) is increasingly used as a tool to bridge the gap between network structure, function, and behavior in fMRI studies. Computational models allow us to bridge the gap even further by directly associating individual neuron activity with behavior. However, most computational models that have analyzed embodied behaviors have employed non-spiking neurons. On the other hand, computational models that employ spiking neural networks tend to be restricted to disembodied tasks. We show for the first time the artificial evolution and TE-analysis of embodied spiking neural networks to perform a cognitively-interesting behavior. Specifically, we evolved an agent controlled by an Izhikevich neural network to perform a visual categorization task. The smallest networks capable of performing the task were found by repeating evolutionary runs with different network sizes. Informational analysis of the best solution revealed task-specific TE-network clusters, suggesting that within-task homogeneity and across-task heterogeneity were key to behavioral success. Moreover, analysis of the ensemble of solutions revealed that task-specificity of TE-network clusters correlated with fitness. This provides an empirically testable hypothesis that links network structure to behavior.
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