Optimization of Simulated System Effectiveness Using Evolutionary Algorithms

Using evolutionary algorithms, a search is performed based on a population where each population member consists of a vector of attribute values and a fitness value. A simulation of a system is run, given a particular set of the member attribute values, producing a fitness value. Fitness measures how well the system achieves its mission objectives. If the fitness has a random component, several runs are made to produce average fitness. The procedure is to select the best members from the population based on average fitness and mutate the member attribute values to produce new population members. Since population member attributes can affect process reaction times, wait logic, or decision logic, a search for the best attribute values over 50 to 100 generations can result in optimal fitness. In order to demonstrate the use of evolutionary algorithms in system optimization, a simple inventory system that has a complex fitness surface is considered.