1. Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme M.Miki T.Hiroyasu K.Hatanaka Doshisha University,Kyoto,Japan

2. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Outline Background Background Optimization Problems Optimization Problems Effects of GA Parameters Effects of GA Parameters Distributed GA Distributed GA Distributed Environment GA Distributed Environment GA Conclusion Conclusion

3. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Disadvantage Background Paralleland Distributed Scheme 1) High Computation Cost 2) Convergence to local minimum 3) Difficult to choose proper GA parameters Effective for 1 and 2 Crossover rate Mutation rate

4. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Background Distributed Environment Scheme Problem on proper setting of GA parameters The performance of GA heavily depends on the GA parameters The performance of GA heavily depends on the GA parameters Proper values of GA Parameters depend on problems Propose a new parameter-free distributed GA

5. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan 5KN Structural Optimization Problems 12 3456 10-Member Truss Objective Minimization of Truss Volume Design Valuables Sectional area of each member Constraints Tensile Strength Tensile Strength Compressive buckling Compressive buckling Displacement at node 6 Displacement at node 6

6. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Constraint on tensile stress Constraint on tensile stress Constraint on Compressive buckling Constraint on displacement Fitness Function Design Variables Sectional area of each member (circular shape) 12Bit ×10 = 120Bits

7. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Experiment on Proper GA Parameters Roulette selection Conservation of Elite Up to 1000 generations Pop. Size 270,2430 0.6 0.3 0.6 1.0 0.3 0.6 1.0 0.3 0.6 1.0 0.3 0.1/L 1/L 0.1/L 1/L 10/L L is the length of the chromosome 9 Combinations applied to SPGA Experiment 9 combinations (3 mutation rates ×3 crossover rates) Comparison based on the average of 10 trials out of 12 trials omitting the highest and the lowest values

8. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan CrossoverRateMutationRate Fitness History in Single Population GA (SPGA)

9. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan CrossoverRateMutationRate Fitness History in Single Population GA (SPGA)

10. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Proper GA Parameters of SPGA Mutation Rate 0.1/L 0.1/L Mutation Rate 1/L 1/L Mutation Rate 10/L 10/L The performance of SPGA depends heavily in the proper choice of GA parameters the proper choice of GA parameters

11. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Multiple Population GA (MPGA) SPGA Population A GA is performed in one entire population. GA MPGA GA Same GAs are performed in multiple sub population

12. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Computation time SPGA GA MPGA Slow Fast

13. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Migration in MPGA BetterWorse Migration Exchange of individuals among sub populations. Randomly selected source and destination sub populations Migration Rate Migration interval Experiment Problem : Same as SPGA MPGA:9 sub populations Migration rate = 0.3 Migration interval = 50 [generations]

14. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Proper GA Parameters fo MPGA Mutation Rate 0.1/L 0.1/L Mutation Rate 1/L 1/L Mutation Rate 10/L 10/L

15. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Comparison between SPGA and MPGA Mutation Rate 0.1/L 0.1/L Mutation Rate 1/L 1/L Mutation Rate 10/L 10/L

16. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Comparison between SPGA and MPGA Mutation Rate 0.1/L 0.1/L Mutation Rate 1/L 1/L Mutation Rate 10/L 10/L

17. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan CrossoverRateMutationRate Effect of Multiple Population Increase in the quality of Solutions. However, proper setting of GA parameters is necessary.

18. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Distributed Environment GA Conventional Environment GA Distributed Environment GA (DEGA) Different GA parameters are used. Same parameters are used. Crossover Rate Mutation Rate Experiment Problem : Same as MPGA 9 Different environments (3 mutation rates ×3 crossover rates) for evaluation

19. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan CrossoverRateMutationRate Pop.size = 270 Worst = 1.38 Avg.1.58 Best = 1.74 Best = 1.78 Avg. 1.70 Worst = 1.58 Effect of DEGA 1.75 Results 1. DEGA outperforms the best SPGA. 2.DEGA provides good performance even comparing to MPGA

20. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan Conclusion (1) The multiple population GA yields better solutions than single population GA because the diversity of individuals are maintained in the multiple population GA during the evolutional process. (2) The distributed environment scheme in the multiple population GA shows a good performance compared to other conventional GA. This scheme does not need to predetermine the GA parameters,and it is very useful for many problems where the proper values of those parameters are not known.

21. Intelligent Systems Laboratory Doshisha University,Kyoto,Japan