How do evolutionary algorithms mimic the process of natural selection to optimize solutions to complex problems?

Evolutionary algorithms are population-based optimization techniques inspired by the process of natural selection and survival of the fittest. These algorithms iteratively evolve a population of candidate solutions through selection, crossover, and mutation operators to search for optimal or near-optimal solutions to complex problems with large search spaces. Evolutionary algorithms are used in optimization tasks such as genetic programming, evolutionary strategies, and particle swarm optimization.