Meta-Analysis Of Genetic Algorithm Implementation For Optimization Of Artificial Neural Network Methods
DOI:
https://doi.org/10.38043/tiers.v4i2.5515Keywords:
Meta-Analysis, artificial intelligence, Artificial Neural NetworkAbstract
One of artificial intelligence methods, which is artificial neural networks, have been widely used in data analysis to make predictions, forecasting, and data classification. The artificial neural network method has convergence or local minimum problems because it requires randomly generated weight values. There is a lot of research that discusses optimization techniques for initiating this initial weight to solve that problem. In this study, a meta-analysis was carried out regarding the implementation of genetic algorithms for optimization of artificial neural network methods. Based on 10 journals that has reviewed in this study, it was concluded that optimization of the genetic algorithm can increase the output value of the artificial neural network by 3.44%, but this genetic algorithm optimization have no significant effect based on the sig (2-tailed) value is 0.595 and t count value is 0.551 that have been obtained and tested using paired samples t-test method with help of SPSS software.
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