This project is a small demo of genetic algorithms. The function to be optimized in this project (called also the fitness function) is the function x => x^n.
- The Selection phase aims to select the fittest individuals.
- The Crossover phase aims to produce new individuals. The method used in this projects is pretty simple: Each child has the first half of his genes from one parent and the other half from the other parent.
- The Mutation phase aims to maintain diversity in the population (and mathematically it is used to escape from local optima)