Abstract
A method to solve optimization with discrete variables by using modified evolution strategies (ESs) is presented. ESs imitate biological evolution and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. An important characteristic of ESs that differs from other conventional optimization algorithms is that in place of a single design point the ESs work simultaneously with a population of design points in the space of variables. This allows for an implementation in a parallel-computing environment. In this paper the modified ESs for solving discrete optimization problems and their parallelization are described.
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