Applications of Gaussian Mutation for Self Adaptation in Evolutionary Genetic Algorithms

TL;DR

This paper explores the use of Gaussian mutation in genetic algorithms to improve their self-adaptation capabilities for solving complex optimization problems, inspired by natural evolution and machine learning.

Contribution

It introduces a novel approach integrating Gaussian mutation into genetic algorithms, enhancing their adaptability and effectiveness in optimization tasks.

Findings

Gaussian mutation improves convergence speed

Enhanced adaptability in genetic algorithms

Potential for solving complex optimization problems

Abstract

In recent years, optimization problems have become increasingly more prevalent due to the need for more powerful computational methods. With the more recent advent of technology such as artificial intelligence, new metaheuristics are needed that enhance the capabilities of classical algorithms. More recently, researchers have been looking at Charles Darwin's theory of natural selection and evolution as a means of enhancing current approaches using machine learning. In 1960, the first genetic algorithm was developed by John H. Holland and his student. We explore the mathematical intuition of the genetic algorithm in developing systems capable of evolving using Gaussian mutation, as well as its implications in solving optimization problems.