A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Orove:2014:AJCICT,
-
author = "Osu Joshua Orove and B. O. Eke and N. E. Osegi",
-
title = "A Multi-Gene Genetic Programming Application for
Predicting Students Failure at School",
-
journal = "African Journal of Computing \& ICT",
-
year = "2014",
-
volume = "7",
-
number = "3",
-
pages = "21--34",
-
month = sep,
-
keywords = "genetic algorithms, genetic programming, Student
Failure Rate, Multi-Gene GP",
-
ISSN = "2006-1781",
-
URL = "https://africanjournalofcomputingict1.files.wordpress.com/2017/08/vol-7-no-3-september-2014.pdf",
-
URL = "https://arxiv.org/abs/1503.03211",
-
size = "14 pages",
-
abstract = "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 expression.",
-
notes = "https://afrjcict.net/",
- }
Genetic Programming entries for
Osu Joshua Orove
B O Eke
E N Osegi
Citations