Neuroevolution untuk optimalisasi parameter jaringan saraf tiruan


  • Hindriyanto Dwi Purnomo Universitas Kristen Satya Wacana
  • Tad Gonsalves Dept. Of Information and Communication Sciences, Sophia University, Japan
  • Teguh Wahyono Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga, Indonesia
  • Pratyaksa Ocsa Nugraha Saian Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga, Indonesia



Neuroevolution, Particle swarm optimization, Neural Network, Tuning


Artificial Neural Network is a supervised learning method for various classification problems. Artificial Neural Network uses training data to identify patterns in the data; therefore, training phase is crucial. During this stage, the network weight is adjusted so that they can recognize patterns in the data. In this research, a neuroevolution approach is proposed to optimize artificial neural network parameters (weight) Neuroevolution is a combination of evolutionary algorithms, including various metaheuristics algorithms, to optimize neural network parameters and configuration. In particular, this research implemented particle swarm optimization as the artificial neural network optimizer. The performance of the proposed model was compared to backpropagation, which uses gradient information to adjust the neural network parameter. There are five datasets used as the benchmark problems. The datasets are iris, wine, breast cancer, ecoli, and wheat seeds. The experiment results show that the proposed method has better accuracy than the backpropagation in three out of five problems and has the same accuracy in two problems. The proposed method is also faster than the backpropagation method in all problems. These results reveal that neuroevolution is a promising approach to improving the performance of artificial neural networks. Further studies are needed to explore more benefits of this approach.


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How to Cite

H. D. Purnomo, T. Gonsalves, T. Wahyono, and P. O. N. Saian, “Neuroevolution untuk optimalisasi parameter jaringan saraf tiruan”, AITI, vol. 20, no. 2, pp. 125–134, Aug. 2023.