This paper examines two strategies in order to improve the performance of multi-objective evolutionary algorithms when applied to problems with many objectives: informed initialization and extreme solutions sub-population. The informed initialization is the inclusion of approximations of extreme and internal points of the Pareto front in the initial population. These approximations, called informed initial solutions, are found using a fast evolutionary or local search algorithm on single objective problems obtained by scalarizing the multiple goals into a single goal by the use of weight vectors. The extreme solutions sub-population is proposed here to keep the best approximations of the extreme points of the Pareto front at any point of the evolution, and the selection scheme is biased to give these solutions slightly higher chances of being selected. Experimental results applying these two strategies in continuous and combinatorial benchmark problems show that the diversity in the final solutions is improved, while preserving the proximity to the Pareto front. Some additional experiments that demonstrate how the number of initial informed solutions affects the performance are also presented.
['Dipankar Dasgupta', 'German Hernandez', 'Andres Romero', 'Deon Garrett', 'Aishwarya Kaushal', 'James Simien']
On the use of informed initialization and extreme solutions sub-population in multi-objective evolutionary algorithms