The importance of scalability when comparing dynamic weighted aggregation and pareto front techniques

Authors:
Grzegorz Drzadzewski;Mark Wineberg
Affiliations:
Computing and Information Science, University of Guelph, Guelph, Ontario, Canada;Computing and Information Science, University of Guelph, Guelph, Ontario, Canada
Venue:
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Year:
2005

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Abstract

The performance of the Dynamic Weight Aggregation system as applied to a Genetic Algorithm (DWAGA) and NSGA-II are evaluated and compared against each other. The algorithms are run on 11 two-objective test functions, and 2 three-objective test functions to observe the scalability of the two systems. It is discovered that, while the NSGA-II performs better on most of the two-objective test functions, the DWAGA can outperform the NSGA-II on the three-objective problems. We hypothesize that the DWAGA's archive helps keep the searching population size down since it does not have to both search and store the Pareto front simultaneously, thus improving both the computation time and the quality of the front.