Otimizacao de Redes Neurais atraves de Algoritmos Geneticos Celulares

TL;DR

This paper introduces a methodology using Cellular Genetic Algorithms to automatically search for compact and high-performing Artificial Neural Networks suitable for classification tasks, addressing configuration challenges.

Contribution

It combines Cellular Automata with Genetic Algorithms to enhance diversity and improve the automatic design of efficient neural networks.

Findings

Effective in finding compact neural networks

Maintains genetic diversity longer

Improves classification performance

Abstract

This works proposes a methodology to searching for automatically Artificial Neural Networks (ANN) by using Cellular Genetic Algorithm (CGA). The goal of this methodology is to find compact networks whit good performance for classification problems. The main reason for developing this work is centered at the difficulties of configuring compact ANNs with good performance rating. The use of CGAs aims at seeking the components of the RNA in the same way that a common Genetic Algorithm (GA), but it has the differential of incorporating a Cellular Automaton (CA) to give location for the GA individuals. The location imposed by the CA aims to control the spread of solutions in the populations to maintain the genetic diversity for longer time. This genetic diversity is important for obtain good results with the GAs.