Decision making features occur in all fields of human activities such as science and technological and affect every sphere of our life. Normally, any engineering problem will have a large number of solutions out of which some are feasible and some are non-feasible. The designer’s task is to get best solution out of the feasible solutions. The complete set of feasible solutions constitutes feasible design space and progress towards the optimal design. In such a case, genetic algorithms are good at taking larger, potentially huge search space and navigating them looking for optimal combinations of things and solutions that may not be find in a life time. Genetic algorithm unlike traditional optimization methods processes a number of designs at same time, uses randomized operators that improves search space with efficient result. This paper dealt with important aspects of GA that includes definition of objective function, representation schemas for solution variables and randomized operators. These aspects drive the problem to optimal solution.
