Research map / Learning theory and algorithms / Combinatorial and search optimization
Solving High-Dimensional Multi-Objective Optimization Problems with Low Effective Dimensions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add random embedding (ReMO) | NSGA-II · ZDT10 | hyper-volume: 0.4176 -> 0.8633 | Helped |
| add random embedding (ReMO) | NSGA-II · ZDT20 | hyper-volume: 0.2259 -> 0.7076 | Helped |
| add random embedding (ReMO) | NSGA-II · ZDT30 | hyper-volume: 0.4248 -> 0.8289 | Helped |
| add random embedding (ReMO) | MOEA/D · ZDT10/20/30 | hyper-volume: ZDT10 0.5935 -> 0.6718; ZDT20 0.4313 -> 0.6931 | Helped |
| add random embedding (ReMO), all dims effective but small | NSGA-II · ZDT1ε/2ε/3ε | hyper-volume: ZDT2ε 0.0939 -> 0.3208 | Helped |
| add random embedding (ReMO), all dims effective but small | MOEA/D · ZDT1ε | hyper-volume: 0.4063 -> 0.3961 | Hurt |
| add random embedding (ReMO), all dims effective but small | MOEA/D · ZDT2ε/ZDT3ε | hyper-volume: ZDT2ε 0.1934 -> 0.2998; ZDT3ε 0.3404 -> 0.3804 | Helped |
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