Using an Evolutionary Algorithm Such as Genetic Algorithm (Ga) to Optimize Linear Equations

An Evolutionary algorithm is a subset of evolutionary computations. Cloning, mutation, recombination, and selection are some of the methods used by the evolutionary algorithm that were inspired by biological evolution. The problem that was addressed in this study is how to find the coefficient values of the variables in linear equations that contain a set of variables, it must equal the right side. After reviewing previous research looking for optimization, genetic algorithm, which will use in this paper, so how are going to use this strategy to solve our problems? Select the best chromosomes from this population. Perform random chromosomal hybridization. Then, within the genes of our chromosomes, produce random mutations. Will send these chromosomes for selection again. The best chromosomes among them are selected again. It crosses and turns again, and the process is repeated until a certain number of generations have passed or find the best option. After applying the genetic algorithm through the Matlab program to the linear equation, which contains six variables, for example, the results were very excellent, as the parameter values were found in a very short time and with high accuracy, completely matching the right side of the equation.