Evolutionary Algorithm Based Techniques to Handle Big Data

Abstract

Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. So Data mining, which has as a goal to extract knowledge from large databases, has become a challenge for this large and complex data set [52]. Other challenges in handling Big data include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. To extract and handle this large amount of knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, making an exhaustive search infeasible. Therefore, efficient search strategies are of vital importance. Search strategies based on Evolutionary algorithms have been applied successfully in a wide range of applications. In this chapter, we discuss about Big data and limitations of standard algorithms handling them, Evolutionary Algorithm and their advantages in handling Big data, Commonly used Evolutionary algorithm—Genetic Algorithm and the various application areas where genetic Algorithm plays evolutionary role in the large and complex search space.