A comparative study of diversity in evolutionary algorithms

For many evolutionary algorithms a key obstacle to finding the global optima is insufficient solution diversity, causing the algorithm to become mired in a local optima. Solution diversity can be influenced by algorithm parameters including population size, mutation operator and diversity preservation techniques. This study examines the combined effect of population size, mutation value and the geography imposed by the combinatorial graphs on a set of five standard evolutionary algorithm problems. A trade off can be seen between the initial diversity of the population size, introduction of new diversity from mutation, and the preservation of diversity from combinatorial graph. With an appropriate fusion of these three factors a level of diversity can be achieved to decrease the time to find the global optima.