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

Shunt compensation and optimization of Nigerian 330Kv 34 bus power system using metaphorless Rao-Type algorithms

Power system networks suffer from power losses and bus voltage violations due to heavy loading at the various buses. As a consequence, there is a need for some sort of compensation at the heavily loaded bus points. In this paper, a novel type of optimizer with fast convergence and simplicity called Rao-1, which is based on metaphorless programming, is presented for the task of optimization with shunt compensation on Nigerian 330-kV power network. The application of this algorithm for optimal shunt compensation of the Nigerian 330kV, 34-bus power transmission network is presented. The results obtained by Rao-1 technique are compared with particle swarm optimization (PSO). The findings show that using shunt compensation generally improves the precision of objective function values even though at some trials there might be deviations. Comparative results also show the competitiveness and efficacy of proposed Rao-1 type algorithm in terms of fitness scores and computational run time