Evolutionary multi-objective optimization for generating artificial creature’s personality

Abstract

This paper proposes the evolutionary generation of an artificial creature's personality by using the concept of multi-objective optimization. The artificial creature has its own genome and in which each chromosome consists of many genes that contribute to defining its personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the artificial creature's personality while manually assigning gene values for the individual genome. Moreover, there needs user's preference to obtain artificial creature's personality by using evolutionary generation. Preference is strongly depend on each user and most of them would have difficulty to define their preference as a fitness function. To solve this problem, this paper proposes multi-objective generating process of an artificial creature's personality. Genome set is evolved by applying strength Pareto evolutionary algorithm (SPEA). To facilitate the individuality of generated artificial creature, complement of (1-k) dominance and pruning method considering deviation are proposed. Obtained genomes are tested by using an artificial creature, Rity in the virtual 3D world created in a PC.