Evolutionary algorithms (EAs) are optimization methods that use principles of biological evolution such as selection, recombination, and mutation.

They work with a population of candidate solutions that is iteratively improved.

In each step, individuals evaluated by fitness are selected, combined, and mutated to produce a new population.

Typical variants include genetic algorithms, evolutionary strategies, and genetic programming.

EAs are particularly suitable for complex, nonlinear problems with large search spaces where classical methods fail.