This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a genetic programming algorithm that refines randomly generated character strategies into better ones using tournament selection. The generated AI characters were tested by 27 human players and were rated according to results, perceived difficulty and how engaging the gameplay was. The main advantages of this procedure are that no prior knowledge of how to code the strategies of the AI character is needed and there is no need to interact with the internal code of the game. In addition, the procedure is capable of creating a wide diversity of players with different strategic skills, which could be potentially used as a starting point to a further adaptive process.

A refined version of the texture potential mapping algorithm is introduced in which a one-dimensional MIP map is incorporated. This has the effect of controlling the maximum number of texture samples required. The new technique is compared to existing texture antialiasing methods in terms of quality and sample count. The new method is shown to compare favorably with existing techniques for producing high quality antialiased, texture-mapped images.