Genetic Algorithms Using Hadoop MapReduce

Data-Intensive Computing (DIC) played an important role for large data set utilizing the parallel computing. DIC model shown that can process large amount of data like petabyte or zettabyte day to day. So these have some sorts of attempts to checkout that how DIC will support the Evolutionary(Genetic) Algorithms. Here we have shown step by step explanation that how the Genetic Algorithms(GA), with different implementation form, will be interpret into Hadoop MapReduce framework. Here the results will give details as how Hadoop is best choice to impel genetic algorithm on large dataset problem and shown how the speedup will be increased using parallel computing. MapReduce is designed for large volume of data set. It is introduced for BigData Analysis and it is used a lots of algorithms like Breadth-First Search, Traveling Salesman problems, Finding Shortest Path problem etc. In this framework two key factor, Map and reducer. The Map which is parallely divided the data into many cluster and each cluster the data is form of key and value. The output of map phase data will goes into intermediate phase where data will be shuffling and sorting. Then using the partitioner for dividing the data parallely in different cluster according to the user. The number of cluster are depends on the number of reducers. The reducers will taking all iteration of data give the results in form of values. In This thesis we also show that we compare our implementation with implementation presented in existing model. These two implementation are compare with ONEMAX (bit counting) PROBLEM. The comparison criteria between two implementation are fitness convergences, stability with fixed number of node, quality of final solution, cloud resource utilization and algorithms scalability