Solving the balance problem of massively multiplayer online role-playing games using coevolutionary programming

• We proposed a coevolutionary design method to solve the balance problem of massively multiplayer online role-playing games.
• We demonstrated the theoretical evidence of why the cooperative coevolution algorithm is usually faster than Simple Genetic Algorithm.
• By enabling the elitist-preserving strategy, we improved the performance of probabilistic incremental program evolution.

In massively multiplayer online role-playing games (MMORPGs), each race holds some attributes and skills. Each skill contains several abilities such as physical damage and hit rate. All those attributes and abilities are functions of the character's level, which are called Ability-Increasing Functions (AIFs). A well-balanced MMORPG is characterized by having a set of well-balanced AIFs. In this paper, we propose a coevolutionary design method, including integration with the modified probabilistic incremental program evolution (PIPE) and the cooperative coevolutionary algorithm (CCEA), to solve the balance problem of MMORPGs. Moreover, we construct a simplest turn-based game model and perform a series of experiments based on it. The results indicate that the proposed method is able to obtain a set of well-balanced AIFs more efficiently, compared with the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA) and the hybrid discrete particle swarm optimization (HDPSO) algorithm. The results also show that the performance of PIPE has been significantly improved through the modification works.

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