Evolutionary algorithm for a genetic robot’s personality

This paper proposes a new concept of the genetic robot characterized by its own robot genome in which each chromosome consists of many genes that contribute to defining the robot’s 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 robot’s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper also proposes an evolutionary algorithm for evolving a genetic robot’s personality (EAGRP) in a mobile phone. EAGRP evolves a gene pool that customizes the robot’s genome so that it closely matches a simplified set of personality features desired by the user. It acts on a two-dimensional individual, composed of artificial chromosomes, upon which a new masking method, the Eliza–Meme scheme, is used to derive a plausible individual given the restricted preference settings. This paper also proposes a crossover method that enables reproduction for the two-dimensional genome. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios.