Investigation of multi-layer neural network performance evolved by genetic algorithms

This paper presents a study on the investigation of multi-layer neural networks (MLNNs) performance evolved with genetic algorithm (GA) for multi-logic training patterns applied to various network functions. Specifically, we have concentrated on the Sigmoid, Step and ReLU functions to evaluate and simulate their performances in the network. We have revealed that GA training gives good training results in evolutionary computation by changing of Sigmoid, ReLU and Step as the activity functions in MLNN performance. Sigmoid function has proved to train all patterns for all outputs without any challenge as compared to ReLU function and Step in this study. We are still trying to see how a ReLU function could be trained with GA for MLNNs performance for the two input and four output training patterns termed as the multi-logic pattern training about multiple training parameters.