Icec2010 presentation

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Icec2010 presentation

  1. 1. INVESTIGATING REPLACEMENT STRATEGIES FOR THE ADAPTIVE DISSORTATIVE MATING GENETIC ALGORITHM Carlos Fernandes1,2 J.J. Merelo1 Agostinho C. Rosa2 1 Department of Architecture and Computer Technology, University of Granada, Spain 2 L aSEEB-ISR-IST, Technical Univ. of Lisbon (IST), Portugal
  2. 2. SUMMARY ADMGA Non-Stationary Fitness Landscapes Motivation Replacement Strategies Results Conclusions and Future Work
  3. 3. Dissortative MatingDissortative Mating  Mating between dissimilar individuals.  Higher diversity. Disruptive effect High selective pressure + high disruption effect parent parent
  4. 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 1111111111111111 1111111100001111 Hamming dist.: 4selection ADMGA differs from the SGA at the recombination stage 4 the number of positions at which the corresponding symbols are different Adaptive Dissortative MatingAdaptive Dissortative Mating GA (ADMGA)GA (ADMGA)
  5. 5. ADMGAADMGA 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 5 diversity is controlling the threshold population-wide elitism (or steady-state)
  6. 6. Stationary Fitness Functions:Stationary Fitness Functions: Scalability with Trap FunctionsScalability with Trap Functions order-2 (k = 2) order-3 order-4 6 non-deceptive nearly-deceptive fully deceptive Scalability with problem size
  7. 7. Alternative Replacement StrategiesThreshold ValueThreshold Value Initial threshold value n = 10,000; l = 10 n = 10; l = 10,000 n = 100 order-2
  8. 8. Dynamic OptimizationDynamic Optimization ProblemsProblems 8
  9. 9. ADMGA: DynamicADMGA: Dynamic Optimization ProblemsOptimization Problems  Better performance on “slower” dynamic problems  The performance degrades as the optimum moves faster 9
  10. 10. MotivationMotivation  Improve ADMGA’s performance on faster problems  Is population-wide elitism a good or bad strategy for fast dynamic problems? 10
  11. 11. Replacement StrategiesReplacement 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) 11
  12. 12. ADMGA: DynamicADMGA: Dynamic Optimization ProblemsOptimization Problems  Yang’s (2003) dynamic problem generator: • frequency of change (1/ε) • severity (ρ) 12  ε : 600, 1200, 2400, 4800, 9600, 19200, 38400  ρ : random  Offline performance: average of the best fitness throughout the run  Statistical tests
  13. 13. TestsTests  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 13
  14. 14. Results TestsTests 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. 15. Results TestsTests RS 2 vs EIGA ε→ 600 1200 2400 4800 9600 19200 38400 onemax − − − ≈ ≈ ≈ ≈ trap ≈ ≈ + + + + + knapsack − − ≈ ≈ ≈ ≈ ≈
  16. 16. Genetic Diverstiy
  17. 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

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