Applications of Gaussian Mutation for Self Adaptation in Evolutionary Genetic Algorithms
It addresses optimization problems in computational methods, but appears incremental as it builds on existing genetic algorithm concepts.
The paper explores the use of Gaussian mutation in genetic algorithms to enhance optimization capabilities, focusing on mathematical intuition and implications for solving problems, but does not report specific results or numbers.
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.