R. Chandra and M. Zhang, “Cooperative Coevolution of Elman Recurrent Neural Networks for Chaotic Time Series Prediction,” Neurocomputing, Vol. 86, 2012, pp. 116-123. doi:10.1016/j.neucom.2012.01.014

has been cited by the following article:

• TITLE: Solving the Balance Problem of On-Line Role-Playing Games Using Evolutionary Algorithms

  AUTHORS: Haoyang Chen, Yasukuni Mori, Ikuo Matsuba

  KEYWORDS: Game Design; Game Balance; Cooperative Coevolutionary Algorithm; Probabilistic Incremental Program Evolution

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.8, August 28, 2012

  ABSTRACT: In on-line role-playing games (RPG), each race holds some attributes and skills. Each skill contains several abilities such as physical damage, hit rate, etc. Parts of the attributes and all the abilities are a function of the character’s level, which are called Ability-Increasing Functions (AIFs). A well-balanced on-line RPG is characterized by having a set of well-balanced AIFs. In this paper, we propose an evolutionary design method, including integration with an improved Probabilistic Incremental Program Evolution (PIPE) and a Cooperative Coevolutionary Algorithm (CCEA), for on-line RPGs to maintain the game balance. 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 efficiently. They also show that in this case the CCEA outperforms the simple genetic algorithm, and that the capability of PIPE has been significantly improved through the improvement work.