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In this paper, an effective layer pattern optimization model using genetic algorithm (GA) is developed in detail.
The present research aims to use available experimental data on patch-anchored joints to develop a new anchorage strength model using genetic programming.
This paper describes a decision support evolution model using Genetic Algorithm (GA) as the evolution algorithm and Computational Fluid Dynamics (CFD) as the evaluation mechanism.
The effectiveness of this approach is validated by comparing the results obtained with the solution of reliability planning model using genetic algorithm optimizer.
In this investigation, a loss increase was estimated by determination of unknown constants in a previously developed loss separation model using genetic algorithms.
In this study, based on the integrated conductivity including conductivity and tensile strength) data obtained by carbon fiber/ABS resin matrix composites experiment, a predicting and optimizing model using genetic algorithm-least squares support vector regression (GA-LSSVR) was developed.
Similar(47)
The model uses genetic algorithm (GA) as optimization technique to find the best mix among power sources, storage systems, and back-up sources to minimize life cycle cost, and renewable power system reliability.
No model used genetic information.
In this study, the mechanical performance of lightweight concrete exposed to high temperature has been modeled using genetic programming.
The fitted exponential-sum models using genetic algorithm can very well describe the measured data.
In the present work VLE data of 3-Methylthiophene with various hydrocarbons were modeled using genetic algorithm.
More suggestions(15)
model utilizes genetic
model using interpretive
model using standard
model using several
model using perceptual
model using different
model using simple
model using frequency-domain
model lacking genetic
model using specific
model using experimental
model using live
model using multi-body
model using tf-idf
model taking genetic
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