Combining Simple and Adaptive Monte Carlo Methods for Approximating Hypervolume
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 896-907 |
| Number of pages | 12 |
| Journal / Publication | IEEE Transactions on Evolutionary Computation |
| Volume | 24 |
| Issue number | 5 |
| Online published | 28 Jan 2020 |
| Publication status | Published - Oct 2020 |
Link(s)
Abstract
The computation of hypervolume is a key issue in multiobjective optimization, particularly, multiobjective evolutionary optimization. However, it is NP-hard to compute the exact hypervolume value. Monte Carlo methods have been widely used for approximating the hypervolume. Observing that the basic Monte Carlo method and the fully polynomial-time randomized approximation scheme (FPRAS) suit different solution sets, we propose a combination of these two methods and show that it performs very well on a number of solution sets.
Research Area(s)
- Approximation algorithms., Hypervolume, Multiobjective optimization
Citation Format(s)
Combining Simple and Adaptive Monte Carlo Methods for Approximating Hypervolume. / Deng, Jingda; Zhang, Qingfu.
In: IEEE Transactions on Evolutionary Computation, Vol. 24, No. 5, 10.2020, p. 896-907.
In: IEEE Transactions on Evolutionary Computation, Vol. 24, No. 5, 10.2020, p. 896-907.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
