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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 5, NO. 1, FEBRUARY 2001 41 Brief Papers_______________________________________________________________________________ An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization Yiu-Wing Leung, Senior Member, IEEE and Yuping Wang Abstract— We design a genetic algorithm called the orthogonal genetic algorithm with quantization for global numerical optimiza-tion with continuous variables. Our objective is to apply methods of experimental design to enhance the genetic algorithm, so that the resulting algorithm can be more robust and statistically sound. A quantization technique is proposed to complement an experi-mental design method called orthogonal design. We apply the re-sulting methodology to generate an initial population of points that are scattered uniformly over the feasible solution space, so that the algorithm can evenly scan the feasible solution space once to lo-cate good points for further exploration in subsequent iterations. In addition, we apply the quantization technique and orthogonal design to tailor a new crossover operator, such that this crossover operator can generate a small, but representative sample of points as the potential offspring. We execute the proposed algorithm to solve 15 benchmark problems with 30 or 100 dimensions and very large numbers of local minima. The results show that the proposed algorithm can find optimal or close-to-optimal solutions. Index Terms— Evolutionary computation, experimental design methods, genetic algorithms, numerical algorithms, numerical op-timization, orthogonal array, orthogonal design. I. INTRODUCTION GLOBAL optimization problems arise in almost every field of science, engineering, and business. Many of these problems cannot be solved analytically, and consequently, they have to be addressed by numerical algorithms. In global optimization problems, the major challenge is that an algorithm may be trapped in the