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In this background, we have designed 22 s -triazine compounds using the best five parameters based 3D-QSAR model built by using genetic function approximation.
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Models were developed using genetic function approximation technique and were extensively validated employing different validation statistics.
Linear QSAR models were developed using genetic function algorithm.
The design methodology is based on the minimization of a cost function by using genetic algorithms.
The fitness of each discrete individual in the population is assessed through the cost function and new-fangled individuals are generated by using genetic operators (Changyu et al. 2007; Gupta and Sexton 1999).
By using genetic algorithms, the agent tracks the user's preferences for a particular product by tuning some parameters inside its evaluation function.
So, for overcoming this disadvantage, by using genetic algorithm, a GA-based method is proposed which considers the misclassification error rate of the classifier in its fitness function and tries to minimize it.
A function called integrated energy exergy (IEE) is proposed to optimize the cycle from energy and exergy views simultaneously by using genetic algorithm.
They solved their model by using genetic algorithm.
In this paper, the optimization is achieved by using genetic algorithms.
Prediction performance of neural network model was optimized by using genetic algorithm.
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