The analysis for optimal design of an air-cooled internal combustion engine cooling fin array by using genetic algorithms (GA) is presented in this study. Genetic Algorithms are robust, stochastic search techniques which are also used for optimizing highly complex problems. In this study, the fin array is of the traditional circular fin type, which is subject to ambient convective heat transfer. The parameters (degrees of freedom) selected for the analysis include the cylinder wall thickness-to radius ratio, fin thickness, fin length, the number of fins, and the local heat transfer coefficient. By using a single objective GA procedure, the heat transfer through the fin arrays is set as the objective function to be optimized with each parameter varied within the physical ranges. Proper population size is selected and the mutations, cross-over and selection are conducted in the GA procedure to arrive at the optimal set of parameters after a certain number of generations. The GA proves to be an effective optimization method in the thermal system component designs when the number of independent variables is large.
