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.)

MRPGA(cont.)

Select the global Optimum individual

Reproduction, mutationand submission ofoffspring to thescheduler of MRPGA, and collection optimumindividual

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 0.2

500 individuals 10 times

Experiments(cont.)

MOAE

MOAE+MRPGA

Conclusion

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