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

Outline Abstract Introduction Related Work Architecture MRPGA Implementation Experiments Conclusion

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

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

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

Architecture

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

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

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

Implementation

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

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

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