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Effect of e-MOEA in NACST/Seq

Effect of e-MOEA in NACST/Seq. 2004. 2. 17. MEC Seminar In-Hee Lee. Probe Design. Working with Bio-Med lab. Probe design for HPV. 19 genes. Pre-defined candidate region. Objectives: hairpin, tm variation, similarity, h-measure (between probes, non-target)

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Effect of e-MOEA in NACST/Seq

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  1. Effect of e-MOEA in NACST/Seq 2004. 2. 17. MEC Seminar In-Hee Lee

  2. Probe Design • Working with Bio-Med lab. • Probe design for HPV. • 19 genes. • Pre-defined candidate region. • Objectives: hairpin, tm variation, similarity, h-measure (between probes, non-target) • Constraint: probe sequence must not occur in non-target gene.

  3. With NSGA-II • Popsize 2000, generation 200 • Took 1 week. • Cross-hybridization: at least 11 cases. • Hyther 사용 • Objective values • Haripin: 3038 • H-measure: 35672 • Similarity: 2469 • Tm variation: 2.09008

  4. With e-MOEA • Popsize 2000, gen: 400000 • Took 1 day.

  5. Further Improvements • Density measure • Epsilon: must find appropriate value by trial and error. • Or adaptive archiving? • Real Pareto-front may not be uniform over objective space.

  6. Further Improvements. • Archive size • Fixed or Infinite. • If fixed, which truncation method? • Dominated solutions. • Random victim – worst choice! • Clustering. • Density based.

  7. Further Improvements. • Performance measure • Compatible and complete unary measure does not exist theoretically. • Practically useful unary measure • Convergence: hypervolume • Diversity: crowding distance • Or problem specific performance measure by simulation….

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