This Ant Colony Optimization algorithms and Genetic Algorithms are actively used in controller design, robotic path planning, design automation, biomedical imaging, data mining, and distribution network planning. This paper introduces a genetic algorithm implementation, an ant colony optimization algorithm implementation, and a method of adapting the parameters for the algorithms during the course of their execution whenever they cease producing better solutions. Additionally, it presents the results of experiments performed with and without the method applied. The obtained research outcomes clearly show that the method has the great potential to improve the solutions arrived at in both types of nature inspired algorithms, though the greater improvement is achieved whenever an algorithm tends to stagnate further from the theoretical optimum as happened with the genetic algorithm as compared to with the ant colony optimization algorithm.