A Study of the Parallelization of a Coevolutionary Multi-objective Evolutionary Algorithm
By Carlos Artemio Coello Coello, Margarita Reyes Sierra. Mexican International Conference on Artificial Intelligence, 2004
419 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/2daf719a5756fa6c22af4288452d00dab937f329
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In Artificial Intelligence
- Yusuke Nojima
- Hisao Ishibuchi
- Ryoji Tanabe
- Hiroyuki Masuda
- Yuki Tanigaki
- Yaochu Jin
- Francisco Herrera
- Ran Cheng
In Computational Theory and Mathematics
- Hisao Ishibuchi
- Yusuke Nojima
- Ryoji Tanabe
- Hiroyuki Masuda
- Yuki Tanigaki
- Yaochu Jin
- Ran Cheng
- Ponnuthurai Nagaratnam Suganthan
Citations in the corpus
- An easy-to-use real-world multi-objective optimization problem suite (2020, methodology): “neral, the Pareto optimal solutions and the Pareto front of a realworld problem are unavailable. For this reason, we do not use quality indicators which require reference objective vectors (e.g., IGD [52] and the -indicator [53]). As in [12, 54], we set the M-dimensional reference vector for the HV ”
- Modified Distance Calculation in Generational Distance and Inverted Generational Distance (2015, methodology): “Among other indicators, the inverted generational distance (IGD [4], [19]) is most frequently used for performance evaluation of EMO algorithms in evolutionary many-objective optimization studies [7], [9], [23].”