Genetic Random Weight Change Algorithm for the Learning of Multilayer Neural Networks

TL;DR

This paper introduces a Genetic Random Weight Change (GRWC) algorithm that enhances neural network training by combining genetic algorithms with random weight adjustments to better find global minima.

Contribution

The paper proposes a novel hybrid optimization method that integrates genetic algorithms with random weight change for improved neural network learning.

Findings

Achieved high accuracy in locating global minima.

Demonstrated improved performance over traditional RWC.

Effective exploration of complex weight spaces.

Abstract

A new method to improve the performance of Random weight change (RWC) algorithm based on a simple genetic algorithm, namely, Genetic random weight change (GRWC) is proposed. It is to find the optimal values of global minima via learning. In contrast to Random Weight Change (RWC), GRWC contains an effective optimization procedure which are good at exploring a large and complex space in an intellectual strategies influenced by the GA/RWC synergy. By implementing our simple GA in RWC we achieve an astounding accuracy of finding global minima.