Mobile game application development using evolutionary algorithms

• Main Author: Ong, Jia Hui
• Format: Thesis
• Language: English; English
• Published: 2014
• Subjects: QA76.75-76.765 Computer software
• Online Access: https://eprints.ums.edu.my/id/eprint/42784/1/24%20PAGES.pdf; https://eprints.ums.edu.my/id/eprint/42784/2/FULLTEXT.pdf; https://eprints.ums.edu.my/id/eprint/42784/

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. Evolutionary Algorithms have never been used on mobile applications, thus it gives a novel idea of utilizing Evolutionary Algorithms in developing applications typically in games for mobile platforms. 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 (EP) 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 EP to generate the rules to be played by the mobile users. Since it involves human users, adding Interactive Evolutionary Algorithm (IEA) together with EP has allowed further rules to be generated that are based on the user’s preferences. The results have proven that EP together with IEA can be utilized successfully on the mobile platform. Through the testing conducted, it has been observed that finding a suitable mutation rate and population size model is 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 have shown that mutation rates of 0.7 and 0.9 generate better results when using a population size model of μ + 3. It was also shown that using a higher mutation rate can yield better results in a shorter time compared to lower mutation rates.