A method to solve discrete optimization problems using evolution strategies (ESs) is described. The ESs imitate biological evolution in nature and have two characteristics that differ from other conventional optimization algorithms: (a) ESs use randomized operators instead of the usual deterministic ones; (b) instead of a single design point, the ESs work simultaneously with a population of design points in the space of variables. The important operators of ESs are mutation, selection and recombination. The ESs are commonly applied for continuous optimization problems. For the application to discrete problems several modifications on the operators mutation and recombination are suggested here. Several examples from the literature are solved with this modified ES and the results compared. The examples show that the modified ES is robust and powerful for discrete optimization problems.

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