Ranking-Dominance and Many-Objective Optimization

• Published in: 2007 IEEE Congress on Evolutionary Computation pp. 3983 - 3990
• Main Authors: Kukkonen, S., Lampinen, J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2007
• Subjects: Evolutionary computation; Genetic algorithms; Hypercubes; Information technology; Random number generation; Sorting
• ISBN: 1424413397; 9781424413393
• ISSN: 1089-778X
• DOI: 10.1109/CEC.2007.4424990

An alternative relation to Pareto-dominance is studied. The relation is based on ranking a set of solutions according to each separate objective and an aggregation function to calculate a scalar fitness value for each solution. The relation is called as ranking-dominance and it tries to tackle the curse of dimensionality commonly observed in multi-objective optimization. Ranking-dominance can be used to sort a set of solutions even for a large number of objectives when the Pareto-dominance relation cannot distinguish solutions from one another anymore. This permits the search to advance even with a large number of objectives. Experimental results indicate that in some cases the selection based on ranking-dominance is able to advance the search towards the Pareto-front better than the selection based on Pareto-dominance. However, in some cases it is also possible that the search does not proceed into direction of the Pareto-front because the ranking-dominance relation permits deterioration of individual objectives. The results also show that when the number of objectives increases, the selection based on just Pareto-dominance without diversity maintenance is able to advance the search better than with diversity maintenance. Therefore, diversity maintenance connives at difficulties solving problems with a high number of objectives.