1. MRPGA ： An Extension of MapReduce for Parallelizing Genetic Algorithm Reporter ：古乃卉

2. Outline Abstract Introduction Related Work Architecture MRPGA Implementation Experiments Conclusion

3. Abstract MapReduce Map and Reduce Genetic Algorithm Iteration MRPGA Extension of MapReduce for Parallelizing Genetic Algorithm

4. Introduction Problems of Parallelized Genetic Algorithm Communication, synchronization, heterogeneity and frequent failures Why MapReduce? Provides a parallel design pattern for simplify application developments How to work? Add a phase for global selection at the end of every iteration of PGAs and a coordinator

5. Related Work PGAs Distributed, coarse grained and fine grained MPI ： not flexible enough for handling heterogeneity and failures MapReduce Phoenix, Hadoop and MRPSO

6. Architecture

7. MRPGA Map, Reduce and Reduce Key ： index of the individual Value ： the individual Allows each of the reduce tasks to collect dependent input without fetching data from a remote machine

8. Key ： individual Value ： just number MRPGA(cont.)

9. Select the global Optimum individual Reproduction, mutation and submission of offspring to the scheduler of MRPGA, and collection optimum individual

10. Implementation

11. Experiments MRPGA runtime system with Aneka An enterprise Grid consisting of 33 nodes Pentium 4 processor 1GB of memory 160GB IDE disk 1 Gbps Ethernet Windows XP

12. 300 individuals 100 generations Simulated cost Avg. evaluation 10 sec. Standard deviation 0.2 500 individuals 10 times Experiments(cont.) MOAE MOAE+MRPGA

13. Conclusion This extension makes PGAs can benefit from the MapReduce model on handling heterogeneity and failures