Multi Objective Optimization Test Case Selection for Non Dominated Sorting Genetic Algorithm (NSGA-II)

Abstract

The selection of the regression testing is performed to reduce the test case from the test suite. The Multi Objective Evolutionary Algorithm (MOEA) reduces the computational complexity and sharing parameter. In this work, the non-dominated sorting based multi objective evolutionary algorithm called as NSGA-II which evaluates the above difficulties. A fast non-dominated sorting algorithm selects the operator, which creates the off spring by combining the parent and child populations. NSGA-II should be used to reduce the execution cost and statement coverage from the test suite. In order to overcome this criterion, the proposed NSGA-II is able to find better solutions in all problems compared to elitist multi objective evolutionary algorithm.