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Icec2010 presentation Presentation Transcript

  • 1. INVESTIGATING REPLACEMENT STRATEGIES FOR THE A DAPTIVE D ISSORTATIVE M ATING G ENETIC A LGORITHM Carlos Fernandes 1,2 J.J. Merelo 1 Agostinho C. Rosa 2 1 Department of Architecture and Computer Technology, University of Granada, Spain 2 L aSEEB-ISR-IST, Technical Univ. of Lisbon (IST), Portugal
  • 2. SUMMARY
    • ADMGA
    • Non-Stationary Fitness Landscapes
    • Motivation
    • Replacement Strategies
    • Results
    • Conclusions and Future Work
  • 3. Dissortative Mating
    • Mating between dissimilar individuals.
    • Higher diversity.
    Disruptive effect High selective pressure + high disruption effect parent parent
  • 4.
    • Chromosomes are alowed to crossover if and only their Hamming Distance is above the threshold value.
    • The threshold self-adapts its initial value, and varies during the run according to the population diversity
    11111111 1111 1111 11111111 0000 1111 Hamming dist.: 4 selection ADMGA differs from the SGA at the recombination stage the number of positions at which the corresponding symbols are different Adaptive Dissortative Mating GA (ADMGA)
  • 5. ADMGA Population New population = Offspring population + best parents Selects two and computes h.d. if h. d. > ts if h. d. ≤ ts Crossover and mutate after n/2 (n is the population size) Updates threshold if (failed matings > successful matings) ts ← ts −1 else ts ← ts +1 diversity is controlling the threshold population-wide elitism (or steady-state)
  • 6. Stationary Fitness Functions: Scalability with Trap Functions order-2 (k = 2) order-3 order-4 non-deceptive nearly-deceptive fully deceptive Scalability with problem size
  • 7. Alternative Replacement Strategies Threshold Value Initial threshold value n = 10,000; l = 10 n = 10; l = 10,000 n = 100 order-2
  • 8. Dynamic Optimization Problems
  • 9. ADMGA: Dynamic Optimization Problems
    • Better performance on “slower” dynamic problems
    • The performance degrades as the optimum moves faster
  • 10. Motivation
    • Improve ADMGA’s performance on faster problems
    • Is population-wide elitism a good or bad strategy for fast dynamic problems?
  • 11. Replacement Strategies
    • RS 1: Original
    • RS 2: Mutated copies of the old solutions
    • RS 3: Mutated copies of the best solution
    • RS 4: Random Immigrants (random solutions)
  • 12. ADMGA: Dynamic Optimization Problems
    • Yang’s (2003) dynamic problem generator:
      • frequency of change (1/ ε )
      • severity ( ρ )
    • ε : 600, 1200, 2400, 4800, 9600, 19200, 38400
    • ρ : random
    • Offline performance: average of the best fitness throughout the run
    • Statistical tests
  • 13. Tests
    • Several mutation probability and population size values.
      • mutation: dissortative mating affects optimal probability
      • population size: avoid extra computational effort
    • binary tournament
    • 2-elitism
    • uniform crossover (p=1.0)
      • Balance disruptive effect and selective pressure
  • 14. Results Tests RS 1 vs GGA RS 2 vs GGA ε -> 600 1200 2400 4800 9600 19200 38400 onemax − − ≈ ≈ ≈ ≈ ≈ trap ≈ ≈ + + + + + knapsack − − ≈ ≈ ≈ + + ε -> 600 1200 2400 4800 9600 19200 38400 onemax − − − − ≈ ≈ ≈ trap − − − ≈ + + + knapsack − − − − − ≈ +
  • 15. Results Tests RS 2 vs EIGA ε -> 600 1200 2400 4800 9600 19200 38400 onemax − − − ≈ ≈ ≈ ≈ trap ≈ ≈ + + + + + knapsack − − ≈ ≈ ≈ ≈ ≈
  • 16. Genetic Diverstiy
  • 17. Conclusions and Future Work
    • Mutating old solutions speeds up AMDGA on dynamic problems
    • Only two parameters need to be adjusted: population size and mutation rate
    • ADMGA is at least competitive with EIGA
    • Performance according to severity
    • Constrained Dynamic Problems