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Diversity Maintenance Behavior on Evolutionary Multi-Objective O ptimization

Diversity Maintenance Behavior on Evolutionary Multi-Objective O ptimization . 2011.11.27 at TEILAB. Presenter : Tsung Yu Ho. Main Point of Today’s Presentation. Introduction multi-objective problems (MOP) Perato Front (non-dominated points)

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Diversity Maintenance Behavior on Evolutionary Multi-Objective O ptimization

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  1. Diversity Maintenance Behavior on Evolutionary Multi-Objective Optimization 2011.11.27 at TEILAB Presenter : Tsung Yu Ho

  2. Main Point of Today’s Presentation • Introduction multi-objective problems (MOP) • Perato Front (non-dominated points) • Evolutionary multi-objective optimization (EMO) • Peratodominance-based fitness evaluation. • Diversity maintenance • Elitism • Diversity maintenance • Want to observe diversity in high dimension (D>4)

  3. Related work • HisaoIshibuchi et.al. ,“A Many-Objective Test Problem for Visually Examining Diversity Maintenance Behavior in a Decision Space”, GECCO 2011 • A 2-D problems space is used for presenting many-objective problems. • Observer “diversity maintenance “ on current well-known EMO, such as NSGA-II, SPEA2

  4. Multi-Objective Problems • Perato Front (non-dominated points) Y X

  5. Evolutionary multi-objective optimization (1) • NSGA – II SPEA2 Fitness assignment Density estimation Y Y 7 0 0 0 0 X X

  6. Evolutionary multi-objective optimization (2) • SMS-EMOA • Hypervolume

  7. What’s the problems • Observe diversity maintenance • 2-D is clear thinking. • Manny-objective problems is hardly observed by using figure. • Need to design a test functions to evaluate diversity maintenance. • It is easy to observe if the problems is mapped to 2-D space.

  8. 2-D distance minimization problems • Buying a house nearest these location. • Convenience stores (Objective 1) • MRT stations (Objective 2) • School (Objective 3) • Park (Objective 4)

  9. 2-D Decision Space : Perato Front • A simple example A Perato Front C B

  10. Adjust Problems • Observe diversity

  11. Experiments

  12. Real world application • The region is the range of perato front

  13. Real World Perato Front • The number of perato front in three part.

  14. What information that should be observed? • Diversity maintenance • Number on difference region of Perato front • Small region of Peratofront • Hypervolume

  15. Experiment Results of distribution • The number of solution in the smallest Pareto region.

  16. Experiment Results of diversity • Observe with three points

  17. Hypervolume • The small value is worst. • The reference points : x {maximum value of each objective in perato front}

  18. Conclusion • A 2-D problems space is used for presenting many-objective problems. • Observe well-known EMO. • The 2-D distance minimization problems. • Adjust the region of Perato front • Can be utilized in the real world application • The observation measurement • Hypervolume • Number on difference region of Perato front • Small region of Perato front

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