Transcript Adaptive Multi-objective Differential Evolution with

Adaptive Multi-objective
Differential Evolution with
Stochastic Coding Strategy
Wei-Ming Chen
2011.12.15
Outline
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DIFFERENTIAL EVOLUTION
“AS-MODE”
EXPERIMENTS AND COMPARISONS
CONCLUSIONS
DIFFERENTIAL EVOLUTION
AS-MODE
 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
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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
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
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
EXPERIMENTS AND COMPARISONS
EXPERIMENTS AND COMPARISONS
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 ?
Q&A
 Thank you.