Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability resilience often poorly recognized. In current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed ap...

Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrea...

The forecasting models for stock market index using computational intelligence such as Artificial Neural networks(ANNs) and Genetic programming(GP), especially hybrid Immune Programming (IP) Algorithm and Gene Expression Programming(GEP) have achieved favorable results. However, these studies, have assumed a static environment. This study investigates the development of a new dynamic decision f...

In this fast computing era, most of the embedded system requires more computing power to complete the complex function/ task at the lesser amount of time. One way to achieve this is by boosting up the processor performance which allows processor core to run faster. This paper presents a novel technique of increasing the performance by parallel HPRC (High Performance Reconfigurable Computing) in...

This paper suggests a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and three well known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi Expression Programming (MEP) and Gene Expression Progr...

Abstract Australia is one of the most bushfire-prone countries. Prediction and management bushfires in bushfire-susceptible areas can reduce negative impacts bushfires. The generation bushfire susceptibility maps help improve prediction main aim this study was to use single gene expression programming (GEP) ensemble GEP with well-known data mining generate for New South Wales, Australia, as a c...

Gene Expression Programming (GEP) aims at discovering essential rules hidden in observed data and expressing them mathematically. GEP has been proved to be a powerful tool for constructing efficient classifiers. Traditional GEP-classifiers ignore the distribution of samples, and hence decrease the efficiency and accuracy. The contributions of this paper include: (1) proposing two strategies of ...

Bushfire susceptibility mapping helps the government authorities predict and provide required disaster management plans to reduce adverse impacts from bushfires. In this paper, we investigated Gene Expression Programming (GEP) ensemble methods create bushfire maps for Victoria, Australia, as a case study. indicate that eastern part of Victoria where forests are predominant has highest probabili...

Article history: Accepted 16 December 2015 Available online xxxx This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models....

In this paper we present two non-parametric approaches used for time series analysis and modeling for a financial time series: the DJIA stock index open values. We used two recently developed algorithms and methods for time series prediction, Gene Expression Programming and Neural Networks because they are suitable for the series that present high variability, as in the present situation. After...