Evolutionary Algorithms

With this approach, candidate solutions to an optimization problem are randomly generated and act as individuals interacting with a larger population. A fitness function determines the quality of the solutions the candidates find as they move about in each iteration. The "best fit" individuals are then chosen for reproduction in the next iteration. This generational process is repeated until the algorithm has evolved to find the optimal solution to the problem.