Hybrid particle swarm optimization-genetic algorithm trained multi-layer perceptron for classification of human glioma from molecular brain neoplasia data

Abstract

Multi-Layer Perceptron (MLP) is among the most widely applied Artificial Neural Networks (ANNs). Multi-Layer Perceptron (MLP) requires specific designing and training depending upon specific applications. This paper deals with the high-dimensional problem of classification of human glioma from Molecular Human Brain Neoplasia Data by designing a Multi-Layer Perceptron (MLP) which is trained through hybridizing Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The results are compared with individual algorithms in terms of convergence rate, Mean Squared Error (MSE) and classification accuracy.