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Optimal Reduction Rate for Multi-Objective Optimization: A Comparative Study of EDA Techniques

This study compares the performance of EDA techniques in optimizing two objectives using controlled elitism. Experiments with different reduction rates show similar convergence with minor differences at trade-off solutions. Evaluation suggests more experiments are needed to determine the necessity of controlled elitism.

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Optimal Reduction Rate for Multi-Objective Optimization: A Comparative Study of EDA Techniques

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  1. H-Measure & Similarity 2003. 4. 3 Weekly MEC Seminar In-Hee Lee

  2. Motivation • Optimizing only 2 objectives • To compare with EDA • Odd sequences were generated.

  3. Several experiments: optimizing both objectives With controlled elitism 1st fronts Whole populations

  4. Several experiments: optimizing both objectives Using original NSGA-II 1st fronts Whole populations

  5. Adjusting Reduction Rate (r) • So far, we set it as 0.65 • As suggested in the original paper. • Should be reduced • Discreteness of fitness space • 5 experiments • r=0.1 to 0.5

  6. Adjusting Reduction Rate (r)

  7. Adjusting Reduction Rate (r)

  8. Adjusting Reduction Rate (r) • Almost similar results • When r=0.3 & 0.4, showed best spread and coverage. • Convergence was similar for all settings. • Minor difference was shown at the extreme trade-off solutions.

  9. Original NSGA-II vs. Controlled Elitism r=0.65

  10. Original NSGA-II vs. Controlled Elitism

  11. Original NSGA-II vs. Controlled Elitism • Overall convergence is similar in most cases. • Especially, when r=0.3. • Do we really need controlled elitism? • Needs more experiments.

  12. Similarity Bound • In most cases, we could not make similarity lower than 200.

  13. Similarity Bound • Similarity is defined as sum of pairs. • Similarity value of each pair was 30~40. • Only two sequence is very similar to each other.

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