Simulation based Hardness Evaluation of a Multi-Objective Genetic Algorithm

Studies have shown that multi-objective optimization problems are hard problems. Such problems either require longer time to converge to an optimum solution, or may not converge at all. Recently some researchers have claimed that real culprit for increasing the hardness of multi-objective problems are not the number of objectives themselves rather it is the increased size of solution set, incompatibility of solutions, and high probability of finding suboptimal solution due to increased number of local maxima. In this work, we have setup a simple framework for the evaluation of hardness of multi-objective genetic algorithms (MOGA). The algorithm is designed for a pray-predator game where a player is to improve its lifespan, challenging level and usability of the game arena through number of generations. A rigorous set of experiments are performed for quantifying the hardness in terms of evolution for increasing number of objective functions. In genetic algorithm, crossover and mutation with equal probability are applied to create offspring in each generation. First, each objective function is maximized individually by ranking the competing players on the basis of the fitness (cost) function, and then a multi-objective cost function (sum of individual cost functions) is maximized with ranking, and also without ranking where dominated solutions are also allowed to evolve.Comment: International Conference on Modeling & Simulation, November, 25-27, 2013, Islamaba

Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games

Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm

Condition Driven Adaptive Music Generation for Computer Games

The video game industry has grown to a multi-billion dollar, worldwide industry. The background music tends adaptively in reference to the specific game content during the game length of the play. Adaptive music should be further explored by looking at the particular condition in the game; such condition is driven by generating a specific music in the background which best fits in with the active game content throughout the length of the gameplay. This research paper outlines the use of condition driven adaptive music generation for audio and video to dynamically incorporate adaptively