Genetic Algorithm Design

In this collaborative research endeavour, my team and I rigorously investigate the complexities of Genetic Algorithms (GAs) with a focus on performance optimization. Utilizing Matlab as our computational tool, we designed a series of experiments to assess how varying key parameters—such as population size, crossover rate, mutation rate, genome length, and the number of runs—affect the algorithm's efficiency and effectiveness.

Our findings reveal that nuanced adjustments in these parameters can have a profound impact on the algorithm's ability to find optimal solutions. For example, higher crossover and mutation rates expedited the discovery of optimal strings, while a reduction in genome length led to quicker outcomes. This research serves as a comprehensive guide for understanding both the limitations and strengths of GAs and offers valuable insights for their application across a wide range of problem-solving scenarios.