The Synthesis of a Stochastic Artificial Neural Network Application Using a Genetic Algorithm Approach

Geretti, Luca; Abramo, A.

doi:10.1016/b978-0-12-385983-9.00001-6

Cited by 4 publications

(4 citation statements)

References 65 publications

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“…There are several techniques used for NNs favourable performance for training ANN such as evolutionary algorithms (EA), Multi-objective hybrid evolutionary algorithms (Qasem, Shamsuddin, & Zain, 2012;Tallón-Ballesteros & Hervás-Martínez, 2011), Genetic algorithms (GA) (Blanco, Delgado, & Pegalajar, 2001;García-Pedrajas, Ortiz-Boyer, & Hervás-Martínez, 2006;Geretti & Abramo, 2011), Particle swarm optimization (PSO) (Al-Shareef & Abbod, 2010;Hong-Bo, Yi-Yuan, Jun, & Ye, 2004;Hongwen & Rui, 2006), deferential evolution (DE) (Slowik & Bialko, 2008;Subudhi & Jena, 2011), an colony optimization (ACO) (Ashena & Moghadasi, 2011;Blum & Socha, 2005), BP d improved BP algorithm (Nawi, Ransing, & Ransing, 2006;Yan, Zhongjun, & Jiayu, 2010). These techniques are used for initialization of optimum weights, parameters, activation function, and selection of a proper network structure.…”

Section: Introductionmentioning

confidence: 99%

“…If the network topology is not carefully selected, the NNs algorithm can get trapped in local minima, or it might lead to slow convergence or even network failure. In order to overcome the disadvantages of standard BP, much global optimization population-based technique [20], GA (Geretti & Abramo, 2011), improved BP (Nawi, et al, 2006), DE (Slowik & Bialko, 2008), BP-ant colony (Chengzhi, Yifan, Lichao, & Yang, 2008), and PSO (Hongwen & Rui, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification

Herawan

Shah

Ghazali

et al. 2013

International Journal of Applied Evolutionary Computation

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The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and Global Artificial Bee Colony Algorithm train Multilayer Perceptron (MLP). This paper investigates the new hybrid technique called Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) algorithm. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome GABC-LM algorithm used in this work to train MLP for the boolean function classification task, the performance of GABC-LM is benchmarked against MLP training with the typical LM, PSO, ABC and GABC. The experimental result shows that GABC-LM performs better than that standard BP, ABC, PSO and GABC for the classification task.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification

Herawan

Shah

Ghazali

et al. 2013

International Journal of Applied Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…A feed forward process iterates for a defined period to reach the best prediction with a reduced amount of computation power and processing time. The neuron function model can be defined as - 64 𝑦𝑦 = ∅(𝐴𝐴(𝑥𝑥))…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The effect of the weight in the loss function for feed-forward propagation can be expressed using the partial derivative, shown in (11) equation. 64 𝜕𝜕𝜕𝜕…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Investigation of Interfacial Property with Imperfection: A Machine Learning Approach

Ferdousi

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Interfacial mechanical properties of adhesive joints are very crucial in board applications, including composites, multilayer structures, and biomedical devices. Establishing traction-separation (T-S) relations for interfacial adhesion can evaluate mechanical and structural reliability, robustness, and failure criteria. Due to the short range of interfacial adhesion such as micro to nanoscale, accurate measurements of T-S relations remain challenging. The advent of machine learning (ML) became a promising tool to predict materials behaviors and establish data-driven mechanical models. In this study, we integrated a state-of-the-art ML method, finite element analysis (FEA), and standard experiments to develop data-driven models for characterizing the interfacial mechanical properties precisely. Macroscale force-displacement curves are derived from FEA with incorporation of double cantilever beam tests to generate the dataset for ML model. The eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are used to determine T-S relations with R2 score of 98.8% and locate imperfections at the interface with accuracy of around 80.8%. The outcome of the XGBoost models demonstrated accurate predictions and fast calculation speed, outperforming several other ML methods. Using 3D printed double cantilever beam specimens, the performance of the ML models is validated experimentally for different materials. Furthermore, a XGBoost model-based package is designed to obtain different adhesive materials T-S relations without creating a database or training a model.

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Resource Allocation in Multiple Energy-Integrated Biorefinery Using Neuroevolution and Mathematical Optimization

Chan

Chen

et al. 2021

Process Integr Optim Sustain

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The Synthesis of a Stochastic Artificial Neural Network Application Using a Genetic Algorithm Approach

Cited by 4 publications

References 65 publications

Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification

Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification

Investigation of Interfacial Property with Imperfection: A Machine Learning Approach

Resource Allocation in Multiple Energy-Integrated Biorefinery Using Neuroevolution and Mathematical Optimization

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