AdaGEP - An Adaptive Gene Expression Programming Algorithm

Băutu, Elena; Băutu, Andrei; Luchian, Henri

doi:10.1109/synasc.2007.51

Cited by 16 publications

(18 citation statements)

References 8 publications

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“…The experiments are performed with the AdaGEP extension [4] we implemented for the gep package of the framework ECJ 2 . The settings for AdaGEP are the same throughout all experiments.The maximum number of genes is set for all experiments to 6, but the algorithm adaptively reaches the appropriate number of genes needed to balance accuracy and simplicity of the solution.…”

Section: Resultsmentioning

confidence: 99%

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Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market

Băutu

Luchian

2010

2010 International Conference on Complex, Intelligent and Software Intensive Systems

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Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate several approaches to construct ensembles based on them. We compare the performance of the obtained classifiers with those of Naive Bayes, Support Vector Machines, Multilayer Perceptron, Decision Table and Random Forrest. The experiments performed on real-world stock market data show that the ensembles of GEPevolved classifier models are competitive to classifiers trained by state-of-the-art machine learning methods.

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Section: Resultsmentioning

confidence: 99%

“…To allow the algorithm to adaptively reach the appropriate level of model complexity, we developed AdaGEP, an adaptive version of GEP, described in detail in [4]. AdaGEP avoids bloating, while evolving perfectly correct mathematical models.…”

Section: Adaptive Gene Expression Programmingmentioning

confidence: 99%

Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market

Băutu

Luchian

2010

2010 International Conference on Complex, Intelligent and Software Intensive Systems

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“…The visualized results and performance of the experiments are shown by Figures 15,16,17,18,19,20,21,22,23 and 24.…”

Section: Parameter Optimization Resultsmentioning

confidence: 99%

“…The EGIPSYS algorithm [14] permitted different-sized chromosomes within a population, which other systems, such as canonical GEP, AdaGep [19], and PGEP-O [3], do not support. However, unlike our proposed methodology, all individuals in an EGIPSYS population were required to have the same number of genes.…”

Section: Crossbreeding and Speciationmentioning

confidence: 99%

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Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Browne

Santos

2010

Applied Computational Intelligence and Soft Computing

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Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.

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Mathematical Methods Applied for Hydro-meteorological Time Series Modeling

Bărbulescu

2016

Studies on Time Series Applications in Environmental Sciences

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AdaGEP - An Adaptive Gene Expression Programming Algorithm

Cited by 16 publications

References 8 publications

Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market

Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Mathematical Methods Applied for Hydro-meteorological Time Series Modeling

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