Forecasting Euro – United States Dollar Exchange Rate with Gene Expression Programming

Antoniou, Maria A.; Georgopoulos, Efstratios F.; Theofilatos, Konstantinos; Likothanassis, Spiridon D.

doi:10.1007/978-3-642-16239-8_13

Cited by 2 publications

(1 citation statement)

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“…The initial eversion of our implementation [11,12] named as jGEPmodel1.0 was applied to the problem of modeling the fatigue of composite materials and the problem of prediction financial time series and the results were satisfactory. In the present paper, we extended our initial implementation by introducing a novel local constant optimization operator, a problem specific fitness function and an adaptive mutation operator.…”

Section: Jgepmodelling20mentioning

confidence: 99%

Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology

Theofilatos

Dimitrakopoulos

Antoniou

et al. 2012

Engineering Applications of Neural Networks

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Abstract. Proteins and their interactions have been proven to play a central role in many cellular processes. Thus, many experimental methods have been developed for their prediction. These experimental methods are uneconomic and time consuming in the case of low throughput methods or inaccurate in the case of high throughput methods. To overcome these limitations, many computational methods have been developed to predict and score ProteinProtein Interactions (PPIs) using a variety of functional, sequential and structural data for each protein pair. Existing computational methods can still be enhanced in terms of classification performance and interpretability. In the present paper we present a novel Gene Expression Programming (GEP) algorithm, named as jGEPModelling 2.0, and apply it to the problem of PPI prediction and scoring. jGEPModelling2.0 is a variation of the classic GEP algorithm to make it suitable for the problem of PPI prediction and enhance its classification performance. To test its efficiency, we applied it to a public available dataset and compared it to two other state-of-the-art PPI prediction models. Experimental results proved that jGEPModelling2.0 outperformed existing methodologies in terms of classification performance and interpretability. (This paper is submitted for the CIAB2012 workshop).

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Section: Jgepmodelling20mentioning

confidence: 99%