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Using Nature-Inspired Metaheuristics to Train Predictive Machines

Author

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  • Vasile GEORGESCU

    (University of Craiova)

Abstract

Nature-inspired metaheuristics for optimization have proven successful, due to their fine balance between exploration and exploitation of a search space. This balance can be further refined by hybridization. In this paper, we conduct experiments with some of the most promising nature-inspired metaheuristics, for assessing their performance when using them to replace backpropagation as a learning method for neural networks. The selected metaheuristics are: Cuckoo Search (CS), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), the PSO-GSA hybridization, Many Optimizing Liaisons (MOL) and certain combinations of metaheuristics with local search methods. Both the neural network based classifiers and function approximators are evolved in this way. Classifiers have been evolved against a training dataset having bankruptcy prediction as a target, whereas function approximators have been evolved as NNARX models, where the target is to predict foreign exchange rates.

Suggested Citation

  • Vasile GEORGESCU, 2016. "Using Nature-Inspired Metaheuristics to Train Predictive Machines," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 5-24.
    • Handle: RePEc:cys:ecocyb:v:50:y:2016:i:2:p:5-24
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    Keywords

    Nature inspired metaheuristics; Hybridizations; Training Neural Networks with metaheuristics; instead of backpropagation; Classifiers; Function Approximators; Bankruptcy prediction; Prediction with NNARX models.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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