Two-Stage Genetic Algorithm for Designing Long Short Term Memory (LSTM) Ensembles

Viswambaran, Ramya Anasseriyil; Chen, Gang; Xue, Bing; Nekooei, Seyed Mohammad

doi:10.1109/cec45853.2021.9504788

Cited by 3 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To alleviate this problem, many studies on methods of evolutionary classes were born. In [10], a two-stage algorithm is designed, where the first stage is designed to get the best performing LSTM structure automatically. During the evolution process, the connection weight inheritance method is used to improve the efficiency, and the second stage designs the ensemble system by choosing a suitable LSTM.…”

Section: Related Workmentioning

confidence: 99%

Underwater Internet of Things-Based Solutions for Intelligent Marine Target Recognition

Jiang

Chen

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to solve the problems of poor data processing ability of underwater hardware equipment and low accuracy of classification algorithms in the existing marine target recognition and detection methods based on sensors and transducers, by combining perception technology, underwater Internet of Things technology, and artificial intelligence, multiple devices could communicate with each other to achieve automatic and intelligent high-precision marine target recognition. Compared with existing methods, not only the accuracy rate is improved but also the hardware requirements are lower, and it is easier to deploy in engineering.

show abstract

Section: Related Workmentioning

confidence: 99%

Underwater Internet of Things-Based Solutions for Intelligent Marine Target Recognition

Jiang

Chen

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Another way to combine NNs with bioinspired algorithms that was approached in the literature is to train multiple LSTM predictors and then choose the best ones and combine their results. In [24], a genetic algorithm is used to select the best trained LSTMs to be combined in an ensemble used to solve classification tasks. The aim of the genetic algorithm is to create a diverse ensemble that is proven to achieve better results than the basic algorithm.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Improvement of LSTM-Based Forecasting with NARX Model through Use of an Evolutionary Algorithm

2022

View full text Add to dashboard Cite

The reported work aims to improve the performance of LSTM-based (Long Short-Term Memory) forecasting algorithms in cases of NARX (Nonlinear Autoregressive with eXogenous input) models by using evolutionary search. The proposed approach, ES-LSTM, combines a two-membered ES local search procedure (2MES) with an ADAM optimizer to train more accurate LSTMs. The accuracy is measured from both error and trend prediction points of view. The method first computes the learnable parameters of an LSTM, using a subset of the training data, and applies a modified version of 2MES optimization to tune them. In the second stage, all available training data are used to update the LSTM’s weight parameters. The performance of the resulting algorithm is assessed versus the accuracy of a standard trained LSTM in the case of multiple financial time series. The tests are conducted on both training and test data, respectively. The experimental results show a significant improvement in the forecasting of the direction of change without damaging the error measurements. All quality measures are better than in the case of the standard algorithm, while error measures are insignificantly higher or, in some cases, even better. Together with theoretical consideration, this proves that the new method outperforms the standard one.

show abstract

Evolutionary Design of Long Short Term Memory Networks and Ensembles through Genetic Algorithms

Viswambaran,

Nekooei,

Chen

et al. 2024

2024 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

Two-Stage Genetic Algorithm for Designing Long Short Term Memory (LSTM) Ensembles

Cited by 3 publications

References 34 publications

Underwater Internet of Things-Based Solutions for Intelligent Marine Target Recognition

Underwater Internet of Things-Based Solutions for Intelligent Marine Target Recognition

Improvement of LSTM-Based Forecasting with NARX Model through Use of an Evolutionary Algorithm

Evolutionary Design of Long Short Term Memory Networks and Ensembles through Genetic Algorithms

Contact Info

Product

Resources

About