The ppt presented at the International Conference on Future Computer and Communication, 2009 at Kuala Lumpur, Malaysia. Includes the early work done in the project: "Evolving Universal Hash Functions using Genetic Algorithms". The revised version of this project was presented at GECCO 2009.

1. Evolving Universal Hash Functions Using Genetic Algorithms Ramprasad Joshi, Mustafa Safdari 2009 INTERNATIONAL CONFERENCE ON FUTURE COMPUTER AND COMMUNICATION BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI GOA CAMPUS

12. Results 2009 INTERNATIONAL CONFERENCE ON FUTURE COMPUTER AND COMMUNICATION TABLE I RESULTS OF RUNNING THE ALGORITHM FOR RANDOM INPUT DISTRIBUTIONS Sr. No. Range Of Input Crossover Type * Mutation Type * No. of keys n No. of buckets N No. of initial collisions n collisions n filled p a b 1. 0-10 1 2 10 10 0 0 10 11 3 2 2. 0-500 1 2 10 11 1 4 6 701 67 452 3. 0-600 1 2 20 23 2 2 18 1013 626 635 4. 0-100 1 1 100 100 0 0 100 179 109 114 5. 0-50000 1 2 100 101 8 21 79 98869 54339 35059 6. 0-1000 1 2 500 499 0 1 499 1823 747 581 7. 0-50000 1 2 500 499 37 108 392 69313 46631 9950 8. 1 2 10000 10000 0 0 10000 14153 9347 517 9. 1 2 10000 10000 0 0 10000 57203 25869 37769 10. 0-50000 1 2 10000 10000 911 2397 6692 79063 33068 31178 * Indices from the crossover and mutation type as mentioned in the previous section