**Abstract**

This chapter presents a heuristic evolutionary optimization algorithm that is loosely based on the principles of evolution and natural genetics. In particular, this chapter describes an evolutionary algorithm that is a hybrid of a genetic algorithm and a differential evolution algorithm. This algorithm uses an elitist, ranking, random selection method, several mutation methods and both two level and three level Taguchi crossover. This algorithm is applied to 13 commonly used global numerical optimization test functions, including a spherical, three hyper-ellipsoid, the sum of different powers, Rastrigin's, Schwefel's, Griewank's, Rosenbrock's valley, Styblinski-Tang, Ackley's Path, Price-Rosenbrock, and Eggholder's functions. This algorithm is applied 1000 times to each of the 13 test functions, and the results shows that this algorithm always converges to each of the 13 test function's global minimum.

**Keywords:** optimization algorithm, differential evolution, genetic algorithm, hybrid, Taguchi crossover
