Adapting MapReduce framework for genetic algorithm with large population

Genetic algorithm (GA) is an algorithm that models inspiration from natural evolution to solve complex problems. GA is renowned for its ability to optimize different types of problem. However, the performance of GA necessitates data and process intensive computing when incorporating large population. This research proposes and evaluates the performance of GA by adapting MapReduce (MR), a parallel processing framework introduced by Google that utilize commodity hardware. The algorithm is executed with population size of up to 10 million. Performance scalability is tested by using 1, 2, 3, and 4 node configurations. The travelling salesman problem (TSP) is chosen as the case study while performance improvement, speedup, and efficiency are employed for performance benchmarking. This research revealed that MR can be naturally adapted for GA. It is also discovered that MR can accommodate GA with large population while providing good performance and scalability.