GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems

D’Angelo, Gianni; Palmieri, Francesco

doi:10.1016/j.ins.2020.08.040

Cited by 127 publications

(43 citation statements)

References 27 publications

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“…The inspiration to undertake research on evolutionary computation (EC) [ 29 ] was the imitation of nature in its mechanism of natural selection, inheritance and functioning. Genetic algorithms (GAs) [ 31 ] are a part of evolutionary computation techniques, which have been used with success in fields such as the vehicle routing problem [ 32 ], feature selection [ 33 ], optimization [ 34 ], heart sound segmentation [ 35 ] or traveling salesman problem [ 36 ].…”

Section: State Of the Artmentioning

confidence: 99%

Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios

Palka

Książek

Pławiak

et al. 2021

Sensors

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This study is focused on applying genetic algorithms (GAs) to model and band selection in hyperspectral image classification. We use a forensic-inspired data set of seven hyperspectral images with blood and five visually similar substances to test GA-optimised classifiers in two scenarios: when the training and test data come from the same image and when they come from different images, which is a more challenging task due to significant spectral differences. In our experiments, we compare GA with a classic model optimisation through a grid search. Our results show that GA-based model optimisation can reduce the number of bands and create an accurate classifier that outperforms the GS-based reference models, provided that, during model optimisation, it has access to examples similar to test data. We illustrate this with experiments highlighting the importance of a validation set.

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Section: State Of the Artmentioning

confidence: 99%

Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios

Palka

Książek

Pławiak

et al. 2021

Sensors

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“…We used dynamic learning factors to combine DLS and BCE more effectively, but there are more ways to combine them more effectively in the future. In terms of the selection of the initial value of the dynamic convergence factor, suggestions in relevant paper [94] can also be referred to get a better initial value.…”

Section: Resultsmentioning

confidence: 99%

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Gui

Wang

Wang³

2021

Mathematics

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Due to the complexity of many-objective optimization problems, the existing many-objective optimization algorithms cannot solve all the problems well, especially those with complex Pareto front. In order to solve the shortcomings of existing algorithms, this paper proposes a coevolutionary algorithm based on dynamic learning strategy. Evolution is realized mainly through the use of Pareto criterion and non-Pareto criterion, respectively, for two populations, and information exchange between two populations is used to better explore the whole objective space. The dynamic learning strategy acts on the non-Pareto evolutionary to improve the convergence and diversity. Besides, a dynamic convergence factor is proposed, which can be changed according to the evolutionary state of the two populations. Through these effective heuristic strategies, the proposed algorithm can maintain the convergence and diversity of the final solution set. The proposed algorithm is compared with five state-of-the-art algorithms and two weight-sum based algorithms on a many-objective test suite, and the results are measured by inverted generational distance and hypervolume performance indicators. The experimental results show that, compared with the other five state-of-the-art algorithms, the proposed algorithm achieved the optimal performance in 47 of the 90 cases evaluated by the two indicators. When the proposed algorithm is compared with the weight-sum based algorithms, 83 out of 90 examples achieve the optimal performance.

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“…Its main use is in eldercare and healthcare applications, especially when it is combined with other technologies, such as the Internet of Things (IoT). HAR can be performed by using many technologies, but nowadays the proliferation of small sized electronic equipment and the large usage of AIand ML-based algorithms in many research and industrial fields [12,[14][15][16], have allowed the spread of HAR solutions by leveraging the built-in sensors of smartphones. Typical human activities that can be recognized by HAR systems are: walking, sleeping, driving, sitting, running, standing, cooking, etc.…”

Section: Human Activity Recognition and Related Workmentioning

confidence: 99%

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images

D’Angelo

Palmieri

2021

Neural Comput & Applic

Self Cite

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With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

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GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems

Cited by 127 publications

References 27 publications

Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios

Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images

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