Evolutionary algorithm for a genetic robot’s personality

This paper proposes a new concept of the genetic robot which has its own robot genome, in which each chromosome consists of many genes that contribute to defining the robotpsilas personality. The large number of genes also allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robotpsilas personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes an evolutionary algorithm for a genetic robotpsilas personality (EAGRP). EAGRP evolves a gene pool that customizes the robotpsilas genome so that it closely matches a simplified set of features desired by the user. It does this using several new techniques. It acts on a 2 dimensional individual upon which a new masking method, the Eliza-Meme scheme, is used to derive a plausible individual given the restricted preference settings desired by the user. The proposed crossover method allows reproduction for the 2-dimensional genome. Finally, the evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios.