1 / 16

Parent Selection Strategies for Evolutionary Algorithms

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

enrico
Download Presentation

Parent Selection Strategies for Evolutionary Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

  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 .1241 .2687 .158 .0651 .13 .1641 .028 .1548 .124 .0951 .2451 .0168 .03 .1953 .1307 .0214 .351 .24 .024 .068 .0954 .167 .0265 .457 .13 .0657 .084 .201 .1634 .157

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

  10. Results NQueen

  11. Results NQueen 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. Results Knapsack 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?

More Related