In evolutionary optimization, replication is like making copies of the best solutions. Imagine a survival contest where top performers are cloned. These copies then undergo changes (mutations) or combine features (crossover) to create new solutions. Replication ensures good traits are passed on, increasing chances that future generations will perform even better. It's used in algorithms to solve complex problems, where keeping and tweaking successful solutions gradually leads to finding the best or a very good answer.