Adaptive Mechanism for GA-NN to Enhance Prediction Model

Abstract

This research presents a hybrid Genetic Algorithm Neural Network (GA-NN) model to replace the physical tests procedures of Medium Density Fiberboard (MDF). Emphasis is on applying an adaptive mechanism on GA to enhance model performance. Data included in the model is MDF properties and its fiber characteristics. The focus of this study is the Multilayer Perceptron NN model, which is reliable to learn from seven inputs fed to the network to produce prediction of three targets. In order to avoid result from local optimum scenario, GA optimizes synaptic weights of the network towards reducing prediction error. The research used a fixed probability rates for crossover and mutation for hybrid GA-NN model. GA-NN model is further improved using adaptive mechanism to help identify the most suitable operator probability rates. The fitness value refers to Sum of Squared Error. Performance comparisons are between hybrid GA-NN and hybrid GA-NN with adaptive mechanism. Results show the hybrid GA-NN model with adaptive mechanism perform better than the ordinary hybrid model. The reliable model is able to simulate the testing procedure and therefore able to reduce the testing time required as well as to reduce the cost. Adaptive mechanism in GA helps increase capability to converge at zero sooner than the ordinary GA