Master-Slave Parallel Genetic Algorithm Based on MapReduce Using Cloud Computing

Abstract

The implementation platforms of parallel genetic algorithms (PGAs) include high performance computer, cluster and Grid. Contrast with the traditional platform, a Master-slave PGA based on MapReduce (MMRPGA) of cloud computing platform was proposed. Cloud computing is a new computer platform, suites for larger-scale computing and is low cost. At first, describes the design of MMRPGA, in which the whole evolution is controlled by Master and the fitness computing is assigned to Slaves; then deduces the theoretical speed-up of MMRPGA; at last, implements MMRPGA on Hadoop and compares the speed-up with traditional genetic algorithm, the experiment result shows MMRPGA can achieve slightly lower linear speed-up with Mapper’s number.