Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation

Sexton, Randall S.; Dorsey, Robert E.; Johnson, John D.

doi:10.1016/s0167-9236(97)00040-7

Cited by 221 publications

(96 citation statements)

References 15 publications

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“…On the other hand, a matrix-based representation of weights, where a column-wise and a row-wise crossover operators were also defined [152]. The GA-based real coded weights optimization outperforms BP and its variants for solving real-world applications [153][154][155][156]. Moreover, an evolutionary inspired algorithm, called differential evolution (DE) [105,157] that imitates mutation and crossover operator to solve complex continuous optimization problems was found to be performing efficiently for real-valued weight vector optimization [158][159][160].…”

Section: Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

442

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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Section: Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

442

154

View full text Add to dashboard Cite

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“…Unlike the backpropagation using gradient method, a GA can avoid local minimum traps while performing a global search for best set of connection weights. Literature has reported on the superiority of a set of network weights selected by GA (Whitley et al, 1990;Sexton et al, 1998).…”

Section: Genetic Algorithms In Ann Optimizationmentioning

confidence: 99%

Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network

Nguyen¹,

Kira²

2018

IJEF

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This study investigates a versatile forecasting technique using an integrated system of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in a mixture-of-experts architecture to solve a general economic forecasting problem involving a mix of temporal and non-temporal variables. Using Klein Model I as a context and previous estimations from traditional methods as benchmarks, the study provides evidence on the effectiveness and efficiency of this integrated system. ANN helps overcome the imposition of assumptions on the behaviors of related variables, the specification of exact relationships, and the difficulty in nonlinear estimations of the economic model. GA helps overcome the sub-optimality of the tedious trial-and-error process in network building. The flexibility of the mixture-of experts network architecture offers many alternative configurations to capture the peculiarities of variables in context before aggregating intermediate estimations into the final result. The integrated system has shown its ability in processing effectively the mixture of economic variables, and producing efficient estimations and forecasts.

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“…Mizuno et al (1998), employ ANN to Tokyo stock exchange to predict buying and selling signals with an overall prediction rate of 63%. Sexton et al (1998) concluded that the use of momentum and start of learning at random points may solve the problems that may occur in training processes. Phua et al (2000), applied neural networks with genetic algorithms to the stock exchange market of Singapore and predicted the market direction with an accuracy of 81%.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Bank Stock Market Prices With a Hybrid Method: The Case of Alpha Bank / Vertybinių Popierių Kainų Prognozavimas Hibridiniu Metodu: Alpha Bank Pavyzdys

Koutroumanidis

Ιωάννου

Zafeiriou

2011

Journal of Business Economics and Management

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Abstract. The present study aims at constructing Confi dence Intervals (C.I) for the predicted values of a Time Series with the application of a Hybrid method. The presented methodology is complicated and thus is completed in different stages. Initially the Artifi cial Neural Networks (ANNs) is applied on the raw time series in order to estimate C.I of the forecasts. Then, the Bootstrap method is employed on the residuals generated by the preceded process. On the upper and lower limit of the estimated C.I., two new ANNs are employed in order to make point estimations (of the upper and lower limits) using of Object Oriented Programming. For the empirical analysis daily observations of the closing prices of Alpha Bank stocks have been used. The sample period is extended from 28/01/2004 until 30/11/2005. The nonstationarity of the time series employed in our study is not a forbidding condition for the estimation of the confi dence intervals, in our case, since the level of bootstrap still provides a satisfactory approximation for the roots arbitrarily close to unity (Berkowitz, Kilian 1996). The accuracy of the forecasts was surveyed with the use of different criteria and the results were satisfactory.

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Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation

Cited by 221 publications

References 15 publications

Metaheuristic design of feedforward neural networks: A review of two decades of research

Metaheuristic design of feedforward neural networks: A review of two decades of research

Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network

Forecasting Bank Stock Market Prices With a Hybrid Method: The Case of Alpha Bank / Vertybinių Popierių Kainų Prognozavimas Hibridiniu Metodu: Alpha Bank Pavyzdys

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