Optimising Real-time Performance of Genetic Algorithm Clustering Method

This paper presents the optimisation of real-time performance of the genetic algorithm clustering method. This performance optimisation concerns the population diversity and limitation and is based on actual runtime of the algorithm. A real-time ticker is incorporated into the algorithm for actual runtime measurement. For population diversity and limitation, a controlled k-means analysis is performed on the population of solutions to determine its diversity. Achieving a less diverse population in less amount of time without sacrificing the accuracy of the algorithm will help reduce the time-complexity of the algorithm, thus opening up the potential for the algorithm to cluster data in higher dimensions. Results from this study will be used for improving the method of clustering fatigue damage features of automotive components using genetic algorithm based methods.