ANN Hyperparameter Optimization by Genetic Algorithms for Via Interconnect Classification

Sanchez-Masis, Allan; Carmona-Cruz, Allan; Schierholz, Morten; Duan, Xiaomin; Roy, Kallol; Yang, Cheng Fu; Rimolo-Donadío, Renato; Schuster, Christian

doi:10.1109/spi52361.2021.9505202

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…To optimize these hyperparameters, the GA is an efficient and robust approach [42]. Derived from the mechanism of biological evolution, the GA uses concepts like natural selection, crossover, and mutation to search for the best combination of hyperparameters for the desired performance of the ANN [43], as illustrated in Figure 3.…”

Section: Artificial Neural Network With Hyperparameters Optimized By ...mentioning

confidence: 99%

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Conte,

Oliveira

2024

Energies

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Global environmental impacts such as climate change require behavior from society that aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels with other energy sources. An important aspect of efficient and sustainable management of the electricity supply in Brazil is the prediction of some variables of the national electric system (NES), such as the price of differences settlement (PLD) and wind speed for wind energy. In this context, the present study investigated two distinct forecasting approaches. The first involved the combination of deep artificial neural network techniques, long short-term memory (LSTM), and multilayer perceptron (MLP), optimized through the canonical genetic algorithm (GA). The second approach focused on machine committees including MLP, decision tree, linear regression, and support vector machine (SVM) in one committee, and MLP, LSTM, SVM, and autoregressive integrated moving average (ARIMA) in another. The results indicate that the hybrid AG + LSTM algorithm demonstrated the best performance for PLD, with a mean squared error (MSE) of 4.68. For wind speed, there is a MSE of 1.26. These solutions aim to contribute to the Brazilian electricity market’s decision making.

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Section: Artificial Neural Network With Hyperparameters Optimized By ...mentioning

confidence: 99%

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Conte,

Oliveira

2024

Energies

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FNNs Models for Regression of S-Parameters in Multilayer Interconnects with Different Electrical Lengths

Sánchez-Masís,

Rimolo-Donadio,

Roy

et al. 2023

2023 IEEE MTT-S Latin America Microwave Conference (LAMC)

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ANN Hyperparameter Optimization by Genetic Algorithms for Via Interconnect Classification

Cited by 2 publications

References 12 publications

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

FNNs Models for Regression of S-Parameters in Multilayer Interconnects with Different Electrical Lengths

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