A differential evolution strategy

This contribution introduces an evolutionary algorithm (EA) for continuous optimization in ℝ
ⁿ
. The algorithm generates new individuals by the standard nonelitist truncation selection and the differential mutation to generate new individuals. The differential mutation is enriched by adding a random vector in the direction of the shift of population midpoint. Difference vectors are generated with the use of the archive of previous populations. Boundary constraints are handled by penalty function.