Review on Genetic Algorithm and Its Application

Genetic algorithm is very powerful technique to find approximate solution to search problems or patterns through application based on biological terms. Genetic algorithms use biologically inspired techniques such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover. The general strengths of genetic algorithms lie in their ability to explore the search space efficiently through parallel evaluation of fitness and mixing of partial solutions through crossover, maintain a search frontier to seek global optima and solve multi-criterion optimization problems. In this paper, Application based on scheduling static tasks in homogeneous parallel system is discussed. Especially uni-processor system would not be sufficient enough to execute all the tasks parallel so it requires an efficient algorithm to determine when and on which processor a given task should execute A static task can be partitioned into a group of subtasks and represented as a DAG (Directed Acyclic Graph), that problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system.