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

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

DIFFERENTIAL EVOLUTION

 Location and Range AS-MODE

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

 Initialization 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 AS-MODE

 Mutation, Crossover and Selection  Mutation and Crossover  Selection : the same way as NSGA-II AS-MODE

 Update values  Range  Probabilities of candidate values AS-MODE

 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 EXPERIMENTS AND COMPARISONS

 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 ? CONCLUSIONS

 Thank you. Q & A