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

2. Evolutionary Algorithms (EAs) Evolutionary Algorithm (EA) Solution User ParametersProblem

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

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 ParameterOptimalTraditional Population Size50050 Offspring Size50 Crossover2-point1-point Mutation Rate.1% Parent SelectionRandom2-1 Tournament Survivor SelectionTruncation

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

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

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

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

23. 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 ) N AG SiSi  S AGE  1  N AG 2P  AGE 2G

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 ABPS-EA1 - ABPS-EA2 - Global best fitness

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

27. Initial Population Size Effect 3 different runs

28. Tracking Population Size and Average Age Same single run

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

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 ParameterValue InitializationEach bit is initialized to either a 0 or 1 with a uniform probability Parent SelectionRandom Survivor SelectionTruncation RecombinationUniform Crossover for SAT and ONEMAX and 2-point crossover for DTRAP Mutation Rate1/l (l being the length of the bitstring) Termination Condition100000 fitness evaluations for SAT and DTRAP, Optimal solution found for ONEMAX

38. Experimental Setup DTRAP SAT ONEMAX