Automatic generation of mobile content in entertainment applications using evolutionary computing

Smartphones have recently become very popular among mobile users. With more powerful smartphones being released by manufacturers around the world, it has encouraged more users to own this gadget. Hence it has opened up a whole new market with its mass among users and attracted a large number of smartphone application developers. It has also attracted the attention of academic researchers to start investigating in this new field. Evolutionary computing has been around for the past few decades and this is a good chance for researchers to try and use evolutionary computing techniques on another platform. Testing on different platforms would allow us to view the capabilities of evolutionary computing to perform under limited computational resources such as that of smartphones. The aim of this research is to develop an application that contains evolutionary computing techniques that will be used by the user as an entertainment tool. Evolutionary Programming is used as the main evolutionary technique in this study. An arcade-type game has been created that serves as a test-bed. This game contains no rules so that it can allow Evolutionary Programming to generate the rules to be played by the mobile users. Since it involves human users, adding Interactive Evolutionary Algorithm together with Evolutionary Programming has allowed further rules to be generated that are based on the user's preferences. The preliminary testing has proven that Evolutionary Programming and lEA can be run on the mobile platform without any major difficulties. Through the testing conducted, it has been observed that finding a suitable mutation rate and population size is very vital. This is due to the probability of the generated rules having the ability to be propelled out of a local optimum with more variety and flexibility in generating different offspring that might contain better results. The results from the experiments has shown that mutation rates of 0.7 and 0.9 generates higher and better results when using a population size of IJ + 3. It was clear that using a higher mutation rate can yield better results in a shorter time compared to lower mutation rates