5,840 research outputs found
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
Gene expression programming approach to event selection in high energy physics
Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic Algorithms and Genetic Programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. It allowed automatic identification of selection rules that can be interpreted as cuts applied on the input variables. The signal/background classification accuracy was over 90% in all cases
Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia
Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
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Prediction of jet engine parameters for control design using genetic programming
The simulation of a jet engine behavior is widely used in many different aspects of the engine development and maintenance. Achieving high quality jet engine control systems requires the iterative use of these simulations to virtually test the performance of the engine avoiding any possible damage on the real engine. Jet engine simulations involve the use of mathematical models which are complex and may not always be available. This paper introduces an approach based on Genetic Programming (GP) to model different parameters of a small engine for control design such as the Exhaust Gas Temperature (EGT). The GP approach has no knowledge of the characteristics of the engine. Instead, the model is found by the evolution of models based on past measurements of parameters such as the pump voltage. Once the model is obtained, it is used to predict the behaviour of the jet engine one step ahead. The proposed approach is successfully applied for the simulation of a Behotec j66 jet engine and the results are presented
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