1. Parent Selection Strategies for Evolutionary Algorithms A Comparison of Parent Selection Strategies Modeled After Human Social Interaction By Michael Ames

2. Motivation  Because existing research is limited, but shows significant improvement in algorithm robustness, additional research is warranted.  Evolutionary algorithms modeled after higher level human social interaction could produce significant improvement in convergence times and individual fitness.

3. Background  Assortative mating – mate selection based on the level of genetic similarities between mates.  Disassortative mating – mate selection based on the level of genetic dissimilarities between mates.

4. Background  Hamming distance - The number of bits which differ between two binary strings.  HD = 3 1100010110 1101011010

5. Goals  Determine difficulty of implementation.  Determine how well the strategies handle premature convergence.  Measure selective pressure.  Rate performance vs. other social strategies  Rate performance vs. existing strategies

6. Test Algorithms  N-Queens  Binary Knapsack

7. 8-Queens Q Q Q Q Q Q Q Q

8. Binary Knapsack 000111000001101 010100101001101.157.1634.201.084.0657.13.457.0265.167.0954.068.024.24.351.0214.1307.1953.03.0168.2451.0951.124.1548.028.1641.13.0651.158.2687.1241 101101111110001010100100111100001010010101101111000011010100011001101100101

9. Approach  Implement the evolutionary algorithms  Implement a separate mate selection object.

10. Results NQueen

11. Tournament – generations:3925 time:115.46 sec AMEA Marriage – generations: 5316 time:168.43 sec AMEA Low Cheat – generations: 4876 time:142.57 sec AMEA High Cheat – generations: 4215 time:127.31 sec

12. Results Knapsack

13. Tournament – generations:6975 best:27.9844 AMEA Marriage – generations: 6350 best:27.7853 AMEA Low Cheat – generations: 6550 best:27.4023 AMEA High Cheat – generations: 6825 best:27.8461

14. Conclusions  Poor performance  Longer time / inferior results

15. Future Work  Develop other evolutionary mating strategies  Implement one of the algorithms with two separate populations dynamic in size, one designated male the other female, then apply the same or similar strategies

16. Any of these?