Using Genetic Algorithm To Evolve Cellular Automata In Performing Edge Detection

TL;DR

This paper explores evolving cellular automata with genetic algorithms to perform edge detection in images, demonstrating how the approach converges to effective solutions over time.

Contribution

It introduces a novel method combining genetic algorithms and cellular automata specifically for image edge detection.

Findings

Genetic algorithms successfully evolve cellular automata for edge detection

The evolved automata converge to accurate edge detection results

The approach demonstrates effective performance in image analysis

Abstract

Cellular automata are discrete and computational models thatcan be shown as general models of complexity. They are used in varied applications to derive the generalized behavior of the presented model. In this paper we have took one such application. We have made an effort to perform edge detection on an image using genetic algorithm. The purpose and the intention here is to analyze the capability and performance of the suggested genetic algorithm. Genetic algorithms are used to depict or obtain a general solution of given problem. Using this feature of GA we have tried to evolve the cellular automata and shown that how with time it converges to the desired results.