@inproceedings{discovery10129908,
       booktitle = {Proceedings of the 2021 Genetic and Evolutionary Computation Conference (GECCO 2021)},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {New York, NY, USA},
          volume = {2021},
           title = {The Effect of Offspring Population Size on NSGA-II: A Preliminary Study},
            year = {2021},
       publisher = {Association for Computing Machinery (ACM)},
           month = {July},
          series = {Genetic and Evolutionary Computation Conference (GECCO)},
             url = {https://gecco-2021.sigevo.org/HomePage},
        abstract = {Non-Dominated Sorting Genetic Algorithm (NSGA-II) is one of the
most popular Multi-Objective Evolutionary Algorithms (MOEA)
and has been applied to a large range of problems.
Previous studies have shown that parameter tuning can improve
NSGA-II performance. However, the tuning of the offspring population size, which guides the exploration-exploitation trade-off in
NSGA-II, has been overlooked so far. Previous work has generally
used the population size as the default offspring population size for
NSGA-II.
We therefore investigate the impact of offspring population size
on the performance of NSGA-II. We carry out an empirical study by
comparing the effectiveness of three configurations vs. the default
NSGA-II configuration on six optimization problems based on four
Pareto front quality indicators and statistical tests.
Our findings show that the performance of NSGA-II can be improved by reducing the offspring population size and in turn increasing the number of generations. This leads to similar or statistically
significant better results than those obtained by using the default
NSGA-II configuration in 92\% of the experiments performed.},
          author = {Hort, M and Sarro, F},
        keywords = {Genetic algorithms, multi-objective optimization, NSGA-II, offspring population}
}