An evolutionary algorithm is an optimization method that repeatedly improves a set of candidate solutions. It evaluates candidates against a goal, selects stronger ones, and creates variations—often through mutation or recombination—to test in later rounds. The process draws on ideas from biological evolution but is a computational search technique, not biological evolution itself.
In cybersecurity, evolutionary algorithms can help explore large design spaces, such as tuning detection rules or testing system configurations. They may also be used to search for inputs that expose weaknesses in software or machine-learning models. Defenders should validate results carefully: optimization depends on its objective and test data, so a solution that scores well in a narrow evaluation may not be robust in real conditions. In circuit design, the same approach can iteratively search for configurations that meet chosen performance goals; its use in any particular example should not be assumed without evidence.