Hort, M;
Sarro, F;
(2021)
The Effect of Offspring Population Size on NSGA-II: A Preliminary Study.
In:
Proceedings of the 2021 Genetic and Evolutionary Computation Conference (GECCO 2021).
Association for Computing Machinery (ACM): New York, NY, USA.
(In press).
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | The Effect of Offspring Population Size on NSGA-II: A Preliminary Study |
| Event: | 2021 Genetic and Evolutionary Computation Conference (GECCO 2021) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://gecco-2021.sigevo.org/HomePage |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Genetic algorithms, multi-objective optimization, NSGA-II, offspring population |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10129908 |
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