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The effectiveness of applying genetic algorithms to combinatorial optimization has been widely demonstrated using many types of benchmark problems, such as the traveling salesman problems and job-shop scheduling problems. We want to optimize strategies for advertising in newspapers sold in Japan. Our problem is to select appropriate newspapers and find the correct frequency of advertising for a product in order to maximize the level of advertising to which the target audience is exposed, within the constraint of a limited total budget. Advertising problems are typically so large and complex that conventional optimization techniques, such as hill-climbing, cannot find sufficiently cost-effective solutions. We show that a genetic algorithm (GA) approach works well for this type of problem. In addition, we demonstrate, through computer simulations, that an extended GA, called the operon-GA, finds better solutions much faster than a simple GA.