Study of Genetic Algorithm, an Evolutionary Approach

Abstract

Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amount of data stored in databases, data warehouses, or other information repositories. To do this process, data mining uses a variety of algorithms according to the specifications of measures and threshold. The results of this analysis are then used to build models based on real world behavior, which are in turn used to analyze incoming data and make predictions about future behavior. Here, we are focusing on one of the efficient evolutionary algorithm called genetic algorithm. This is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. Genetic algorithms are numerical optimization algorithms inspired by both natural selection and natural genetics. This method is a general one, capable of being applied to an extremely wide range of problems. In this paper we will discuss the Genetic algorithm techniques and its application in data mining in detail.