G. Drzadzewski and M. Wineberg (Canada)
Multi-Objective Evolutionary Algorithms, Genetic
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 test functions. The
performance of the algorithms is evaluated by examining
the spacing, diversity and coverage of the Pareto front, as
well as each algorithm’s execution time. It is discovered
that, while the NSGA-II performs better on most of the
test functions, the DWAGA can outperform the NSGA-II
on some of the functions, including a concave one.