In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.[1]
This code contains the implementation of a type of genetic algorithm, it is Eclectic Genetic Algorithm EGA[2]. Since an aptitude function has a linear equation, it has 2 equations with 2 variables.
[1] wikipedia/Genetic_algorithm.
[2] Kuri, Angel & Quezada, Carlos. (1998). A universal eclectic genetic algorithm for constrained optimization. Proceedings 6th European Congress on Intelligent Techniques & Soft Computing, EUFIT'98.
Project link: https://github.com/amigo20th/EGA-Linear-Equations