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Optimal Number of Genders in EA for Different Types of Problems

Optimal Number of Genders in EA for Different Types of Problems. CS448 Term Project Instructor: Daniel R. Tauritz Yin Liang November 28 th , 2005. Motivation. Multiple-genders are rarely explored and even experimented now Premature Convergence Equilibrium State Keep Balance. Main Goal.

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Optimal Number of Genders in EA for Different Types of Problems

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  1. Optimal Number of Genders in EA for Different Types of Problems CS448 Term Project Instructor: Daniel R. Tauritz Yin Liang November 28th, 2005

  2. Motivation • Multiple-genders are rarely explored and even experimented now • Premature Convergence • Equilibrium State • Keep Balance

  3. Main Goal • Different selection schemes for different genders • Explore the optimal results using multiple genders • Intend to build a decision tree based on the results

  4. Design • No Specific Representations • Initialize Population Randomly • Parent Selection (Multiple Genders) • Reproduction • Crossover between genders • Mutation in each child • Survival Selection • Age-based selection • Keep the fittest individual

  5. Crossover Between Genders

  6. Diagram of EAs with Multiple Genders

  7. Different Representations (Cont’d) • Binary String (Assignment 1) Para1: single gender (20-TS) Para 2: 2 genders(Random+20-TS)

  8. Para 1: get the optimal values after 4347 generations on average • Para2: get the optimal values after 5011 generations on average • Para1: the maximum fitness through 30 runs is 966 • Para2: the maximum fitness through 30 runs is 968 • Statistically speaking, there is no difference between Para1 and Para2 by using t-test.

  9. Integer Vector (Assignment2) Para 1: single gender (4-TS) Para 2: 2 genders (Random+4-TS)

  10. Para 1: get the optimal values after 57 generations on average • Para2: get the optimal values after 87 generations on average • Para1: the maximum fitness through 5 runs is -0.411818 • Para2: the maximum fitness through 5 runs is -0.379094 • Statistically speaking, there is no difference between Para1 and Para2 by using Wilcoxon rank sum test.

  11. Different Population Size • Pop Size = 50 Mutation Chance = 0.001 • Para 1: single gender (10-TS) • Para 2: 2 genders (Random+10-TS) • Para 3: 3 genders (Random+FPS+10-TS)

  12. Statistically speaking, there is no difference among Para1, Para2 and Para3 by using t-test. • Single gender is enough to produce the optimal results in the population size of 50

  13. Pop Size = 200 Mutation Chance = 0.001 • Para 1: single gender (80-TS) • Para 2: 2 genders (Random+80-TS) • Para 3: 3 genders (Random+FPS+80-TS)

  14. Statistically speaking, there is no difference among Para1, Para2 and Para3 by using t-test. • In 30 runs, the maximum fitness can be obtained by using 3 genders.

  15. Pop Size = 400 Mutation Chance = 0.001 • Para 1: single gender (80-TS) • Para 2: 2 genders (Random+80-TS) • Para 3: 3 genders (Random+FPS+80-TS)

  16. Statistically speaking, there is significant difference between Para1 and Para2 but no difference between Para2 and Para3 by using t-test. • In 30 runs, the maximum fitness can be obtained by using 2 or 3 genders.

  17. Different Mutation Chance • Mutation Chance = 0.005 Population Size = 100 • Para 1: single gender (40-TS) • Para 2: 2 genders (Random+40-TS) • Para 3: 3 genders (Random+FPS+40-TS)

  18. Statistically speaking, there is significant difference among Para1, Para2 and Para3 by using t-test. • In 30 runs, the maximum fitness can be obtained by using 2 genders.

  19. Mutation Chance = 0.005 Population Size = 200 • Para 1: single gender (80-TS) • Para 2: 2 genders (Random+80-TS) • Para 3: 3 genders (Random+FPS+80-TS)

  20. Statistically speaking, there is significant difference among Para1, Para2 and Para3 by using t-test. • In 30 runs, the maximum fitness can be obtained by using 2 genders.

  21. Conclusion • Representations should not be included in choosing multiple genders. • For population size less than 400, there is no difference by using multiple genders from the prospective of statistics. But the maximum fitness will obtain by using the certain number of genders in the limited number of runs.

  22. Conclusion • For population size equal to or greater than 400, using 2 or 3 genders will improve the efficiency of EAs at the same level. • For the large mutation chance, 2 genders will be able to keep the balance between selection pressure and genetic diversity better than 1 or 3 genders.

  23. Decision Tree

  24. Questions?

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