A genetic algorithm approach to large scale combinatorial optimization problems in the advertising industry

Abstract

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.