An integrated approach to achieving optimal design of computer games

In a time-to-market environment, designers may not be able to incorporate all the design features in a computer game. For each feature, there are several levels of implementation, which is corresponded to different levels of benefit as well as cost. Therefore, a trade-off decision for determining appropriate levels of implementation is very important, yet has been rarely studied in literature. This paper presents an approach to solve the trade-off decision problem. This approach applies the neural network technique and develops a genetic algorithm to optimize the design of computer games. By this approach, a near-optimal design alternative can be identified in a timely fashion.