Day 13: Configuring the Genetic Algorithm Loop in C#
A genetic algorithm is only as effective as the loop that drives it. While selection, crossover, mutation, and elitism form the backbone of a genetic algorithm (GA), it is the configuration of the evolution loop that determines how the algorithm behaves over time.
Today’s focus is on designing and implementing the loop that runs your genetic algorithm. We will examine how to parameterize the loop with values such as population size, mutation rate, elite count, and maximum generations, and how to structure your logic to support reusable, testable, and adaptable evolutionary flows in C#.