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Adaptive Multi-objective Differential Evolution with Stochastic Coding Strategy

Adaptive Multi-objective Differential Evolution with Stochastic Coding Strategy. Wei-Ming Chen 2011.12.15. Outline. DIFFERENTIAL EVOLUTION “AS-MODE” EXPERIMENTS AND COMPARISONS CONCLUSIONS. DIFFERENTIAL EVOLUTION. AS-MODE. Location and Range. AS-MODE. Many possible Fs and CRs

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Adaptive Multi-objective Differential Evolution with Stochastic Coding Strategy

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  1. Adaptive Multi-objective Differential Evolution with Stochastic Coding Strategy Wei-Ming Chen 2011.12.15

  2. Outline DIFFERENTIAL EVOLUTION “AS-MODE” EXPERIMENTS AND COMPARISONS CONCLUSIONS

  3. DIFFERENTIAL EVOLUTION

  4. AS-MODE Location and Range

  5. AS-MODE Many possible Fs and CRs If it performs better, use it more in next generation !

  6. AS-MODE Initialization

  7. AS-MODE • Updating operation • Select one population • Find the neighbors • Is any one of the neighbors dominates the population ? • Yes : extend the range • No : reduce the range • Add “good neighbors” into next generation

  8. AS-MODE

  9. AS-MODE • Mutation, Crossover and Selection • Mutation and Crossover • Selection : the same way as NSGA-II

  10. AS-MODE • Update values • Range • Probabilities of candidate values

  11. EXPERIMENTS AND COMPARISONS IGD : judge the quality of solution P* : a set of solution is uniformly distributed along the Pareto front P : the points of our solution d(v, P) : the shortest distance between v and points in P

  12. EXPERIMENTS AND COMPARISONS

  13. EXPERIMENTS AND COMPARISONS

  14. CONCLUSIONS • stochastic coding strategy • makes individuals easier detect their surrounding region • Multi mutation factor F and crossover probability CR • make populations can adjust to better algorithm • Efficiency • a little worse than NSGA-II in single generation • maybe can reduce total generation • Better ?

  15. Q & A Thank you.

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