1. Population Sizing as an Emergent Behavior Jason Cook

2. Motivation  Ease of use  Limit necessary manual tuning  Potentially improve performance

3. Problem Statement  Remove the population size parameter  Introduce no new parameters  Maintain useful level of accuracy

4. Solution Method  Set up the population size as an emergent behavior  Replace survivor selection with a survival chance  SR i = (F i – F min) / (F max – F min )

5. Experimental Setup  Compare with a traditional EA  Two main test problems: Griewank Function and D-TRAP Problem  Survival Method:  Survival Chance or or  N-Tournament  Other operators and parameter held constant for both EAs

6. Griewank Function  N dimensional minimization problem  Many local optima  Optimal solution: x i = 0

7. D-TRAP  250 4-bit pieces  Each piece is worth:  3 – uif u ≤ 3  4otherwise  Optimal solution: x i = 0

8. Results

9. Results (Continued):

12. Analysis  Performs as well or better than a traditional EA  Still affected by the initial population size

13. Future Work  More test problems  Use a more competitive EA to test against

14. Questions?