ABSTRACT
The genetic algorithm (GA) is a computerized search and optimization method based on the mechanics of natural genetics and natural selection. In a broader sense, a GA is any population-based model that uses selection and recombination operators and mutation operators to generate new sample points in a search space. This chapter discusses linear mathematical relations which have been developed from the results of Taguchi design of experiments and analysis of variance between input parameters like blank-holder force, friction coefficient, die profile radius, and punch nose radius. The numerical simulation was carried out with the optimum parameters achieved after optimization applying GA. Many crossover operators exist in the GA literature. In the crossover phase, new strings are created by exchanging information among strings of the mating pool. The GA combines the concept of survival of the fittest among string structures with a structured yet randomized information exchange, with some of the innovative flair of human research.
