A case study on the application of a genetic algorithm for optimization of engine parameters

Optimization of modern engines is becoming involved because of both the stringent emission norms and the newly found ability to control various parameters in every zone of the map of engines using electronic fuel injection systems, in both steady state and transient conditions. This paper describes a genetic algorithm and its application to engine optimization when a fitness criterion can be described quantitatively. A specific case study on the selection of a simple speed-dependent injection timer for a diesel engine is described. A population of timers is randomly created where the survival depends on the fitness criterion. From the fit and asexual parents that are randomly selected, offspring are produced to replace the least-fit portion of the population belonging to the previous generation. A small percentage of the population is allowed to mutate. The fitness of the population improves every generation and, in the advanced generation, the fittest timer seems to be the most optimum.