Further Enhancement of Planned Random Algorithm Applied in Character Creation for Video Games

Abstract

This paper introduces the Further Enhanced Planned Random Algorithm (FEPR Algorithm) applied in character creation for video games, addressing limitations of the previous Enhanced Planned Random Algorithm by employing various enhancements. The study's purpose was to further increase the apparent randomness by reducing the repetitions of data results. Employing an experimental quantitative research design, the study used the C++ programming language and libraries such as the “random” library which includes the Mersenne Twister PRNG (Pseudocode Random Number Generator). The effectiveness of the FEPR Algorithm was tested against the 2022 Enhanced Planned Random Algorithm through a series of 1000 iterations in 5 test cases, evaluating the randomness of the generated sequences. Results indicated an 84.47% reduction in pattern repetitions in both the total and average repetitions with the FEPR Algorithm compared to the control. This study's conclusion posits that the FEPR Algorithm successfully further increases the apparent randomness compared to the EPR algorithm. Recommendations include extending the research to other gaming elements and fields where randomness is crucial. The research implications discuss the advancement of randomization processes in algorithmic design. Practical implications highlight the potential for improved game design and player experience, while social implications consider the broader impact on digital entertainment and diversity.