Optimization of a competitive learning neural network by genetic algorithms

Guervós, Juan Julián Merelo; Paton, Michael; Cañas, Alberto J.; Prieto, Alberto; Morán, Federico

doi:10.1007/3-540-56798-4_145

Cited by 22 publications

(14 citation statements)

References 4 publications

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“…This may lead to poor predicting accuracy and unstable forecast performance. However, this can be partly overcome by applying genetic algorithm optimization methods [55] and/or particle swarm optimization [56] algorithm.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

Fan¹,

Ji²,

Wilmsmeier³

et al. 2013

JTTs

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Baltic Exchange Dirty Tanker Index (BDTI) is an important assessment index in world dirty tanker shipping industry. Actors in the industry sector can gain numerous benefits from accurate forecasting of the BDTI. However, limitations exist in traditional stochastic and econometric explanation modeling techniques used in freight rate forecasting. At the same time research in shipping index forecasting e.g. BDTI applying artificial intelligent techniques is scarce. This analyses the possibilities to forecast the BDTI by applying Wavelet Neural Networks (WNN). Firstly, the characteristics of traditional and artificial intelligent forecasting techniques are discussed and rationales for choosing WNN are explained. Secondly, the components and features of BDTI will be explicated. After that, the authors delve the determinants and influencing factors behind fluctuations of the BDTI in order to set inputs for WNN forecasting model. The paper examines non-linearity and non-stationary features of the BDTI and elaborates WNN model building procedures. Finally, the comparison of forecasting performance between WNN and ARIMA time series models show that WNN has better forecasting accuracy than traditionally used modeling techniques.

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Section: Discussionmentioning

confidence: 99%

Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

Fan¹,

Ji²,

Wilmsmeier³

et al. 2013

JTTs

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show abstract

“…When a genetic operator changes an MLP, it considers each hidden neuron (and its input and output weights) as a gene, so that if two MLPs are crossed, complete hidden layer neurons are interchanged (and weights to and from it are treated as one unit) [43], [44], [45].…”

Section: The Methodsmentioning

confidence: 99%

Evolutionary system for prediction and optimization of hardware architecture performance

Castillo

Merelo

Moretó

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Abstract-The design of computer architectures is a very complex problem. The multiple parameters make the number of possible combinations extremely high. Many researchers have used simulation, although it is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using evolutionary multilayer perceptron (MLP) to compute the performance of an architecture parameter settings. Instead of exploring the search space, simulating many configurations, our method randomly selects some architecture configurations; those are simulated to obtain their performance, and then an artificial neural network is trained to predict the remaining configurations performance. Results obtained show a high accuracy of the estimations using a simple method to select the configurations we have to simulate to optimize the MLP. In order to explore the search space, we have designed a genetic algorithm that uses the MLP as fitness function to find the niche where the best architecture configurations (those with higher performance) are located. Our models need only a small fraction of the design space, obtaining small errors and reducing required simulation by two orders of magnitude.

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“…When a genetic operator changes a MLP, it considers each hidden neuron (and its input and output weights) as a "gene", so that if two MLPs are crossed, complete hidden layer neurons are interchanged (and weights to and from it are treated as one unit), as proposed in [25,31].…”

Section: Gprop Methodsmentioning

confidence: 99%

“…ral net [34]. Several authors have suggested evolving ANNs by coding the weights and learning parameters of the individuals of an evolutionary algorithm (EA), pre-establishing the number of neurons and the connectivity between them [21,25]. However, these representations can lead to a lack of precision by restricting the search to just an area of the possible space.…”

Section: Introductionmentioning

confidence: 99%

Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks

García

Valdivieso

Merelo

et al. 2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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In this work we compare two soft-computing methods for producing models that are able to predict whether a company is going to have book losses: artificial neural networks (ANNs) and genetic programming (GP). In order to build prediction models that can be applied to an extensive number of practical cases, we need simple models which require a small amount of data. Kohonen's self-organizing map (SOM) is a non-supervised neural network that is usually used as a clustering tool. In our case a SOM has been used to reduce the dimensions of the prediction problem. Traditionally, ANNs have been considered able to produce better classifier structures than GP. In this work we merge the capability of GP for generating classification trees and the feature extraction abilities of SOM, obtaining a classification tool that beats the results yielded using an evolutionary ANN method.

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Optimization of a competitive learning neural network by genetic algorithms

Cited by 22 publications

References 4 publications

Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

Evolutionary system for prediction and optimization of hardware architecture performance

Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks

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