Discretization Of Decision Variables In Optimization Algorithms

Abstract

This paper presents a verification of universal method for discretization of decision space in optimization algorithms. Real-world optimization tasks frequently use discontinuous decision variables and in order to effectively optimize such tasks, it is necessary to exploit an optimization algorithm that meets such requirement. Unfortunately, very few evolutionary algorithms can naturally work with discontinuous decision space. The method that entitles all optimization algorithms to effectively solve problems with discrete variables is here described and experimentally verified.