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EC Awards Lecture ~ Spring 2008 Advances in Parameterless Evolutionary Algorithms

EC Awards Lecture ~ Spring 2008 Advances in Parameterless Evolutionary Algorithms. Lisa Guntly André Nwamba Research Advisor: Dr. Daniel Tauritz Natural Computation Laboratory. Evolutionary Algorithms (EAs). User Parameters. Problem. Evolutionary Algorithm (EA). Solution.

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EC Awards Lecture ~ Spring 2008 Advances in Parameterless Evolutionary Algorithms

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  1. EC Awards Lecture ~ Spring 2008Advances in Parameterless Evolutionary Algorithms Lisa Guntly André Nwamba Research Advisor: Dr. Daniel Tauritz Natural Computation Laboratory

  2. Evolutionary Algorithms (EAs) User Parameters Problem Evolutionary Algorithm (EA) Solution

  3. Evolutionary Algorithms Create Initial Population Evaluate Fitness No Select Parents Termination Create Offspring Yes Solution Select Survivors Evaluate Fitness

  4. Motivation • Parameter specification complicates EAs • Expert knowledge required • Time-consuming • Sub-optimal - optimal parameter values can change during a run

  5. The Effects of Parameter Values

  6. Parameterless EAs: Our Approach • Completely Parameterless EAs • Biological metaphors may be useful • Typical parameters: • Population size • Parent selection operators • Offspring size • Survival selection • Mutation operators • Crossover operators

  7. Futility-Based Offspring Sizing (FuBOS) André Nwamba

  8. FuBOS: Futility-Based Offspring Sizing • Minimize wasted computation effort

  9. Approach • Look at change in average fitness of the offspring • Average fitness of all n offspring • Average fitness of n-1 previously created offspring • Threshold value

  10. Experimental Setup • Compared FuBOS-EA and manually tuned EA (OOS-EA) • FuBOS-EA uses ε=.001 • Test problems: DTRAP, SAT, and ONEMAX • Used population sizes of 100, 500, 1000 • All tests used same parameters • Performance compared using One-Way ANOVA with significance level of .05

  11. Results

  12. Results

  13. Results

  14. Results

  15. Results

  16. Conclusions • Competitive performance • Extra parameter

  17. FuBOS Future Work • The “epsilon problem” • Genetic Diversity • Parent Selection • Combine with dynamic population sizing

  18. Age-Based Population Sizing (ABPS) Lisa Guntly

  19. The Importance of Age • Age significantly impacts survival in natural populations

  20. F i S S i AGE F B Methods • Survival chance (Si) of an individual is based on age and fitness • Main Equation Fitness of i = Best Fitness

  21. S R ( AGE ) AGE A Survival Chance from Age • Age is tracked by individual, and is incremented every generation • Two equations explored for SAGE • Equation 1 (ABPS-EA1): linear decrease 1 = - Rate of decrease from age

  22. N AG S AGE Survival Chance from Age (cont’d) • Equation 2 (ABPS-EA2): more dynamic Number of individuals in the same age group AGE 1 = - - 2 P 2 G Population size Number of generations the EA will run

  23. N AGE = - - AG S 1 AGE 2 P 2 G Survival Chance from Age (cont’d) • Effects of • More individuals of the same age will decrease their survival chance • Age will decrease survival chance relative to the maximum age (G) NAG Si

  24. Experimental Setup • Testing done on TSP (size 20/40/80) • Offspring size is constant • Compared to a manually tuned EA • Examine effects of • Initial population size • Offspring size • Tracked population statistics • Size • Average age • Global best fitness (GBF)

  25. Performance Results - TSP size 20 Average over 30 runs Global best fitness ABPS-EA1 - ABPS-EA2 -

  26. Performance Results - TSP size 40 Average over 30 runs Global best fitness ABPS-EA1 - ABPS-EA2 -

  27. Initial Population Size Effect 3 different runs

  28. Tracking Population Size and Average Age Same single run

  29. F F F i i W S S AGE AGE F F F B B W Equation with Fitness Scaling • Attempt to fix the lack of selection pressure from fitness • New Main Equation Fitness of i - Fitness Scaling S S = = i i - Best Fitness Worst Fitness

  30. Initial Performance Analysis from Fitness Scaling Equation Average over 30 runs using Global best fitness

  31. Initial Performance Analysis from Fitness Scaling Equation (cont’d) • Independence from initial population size was maintained • Dynamic adjustment of population size during the run was improved • Additional selection pressure from elitism improved performance slightly

  32. ABPS Conclusions • Independence from initial population value was achieved • Autonomous adjustment of population size during a single EA run was successful • Fitness scaling is needed for ABPS to work on more difficult problems

  33. ABPS Future Work • Further exploration of fitness scaling methods • Test on other difficult problems • Compare to other dynamic population sizing schemes • Implement age-based offspring sizing

  34. Impact

  35. Impact • Increases industry usability • Higher performance EAs • Progress towards completely parameterless EA

  36. Questions?

  37. FuBOS Experimental Setup

  38. Experimental Setup • DTRAP • SAT • ONEMAX

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