Behavioral selection in domestic assistance robots: A comparison of different methods for optimization of utility functions

In this paper, the performance of several evolutionary algorithms (EAs), involving different operators, is investigated in connection with the utility function (UF) method, a method for generating behavioral organization (selection) systems in autonomous robots. The standard UF method, which uses an ordinary genetic algorithm (GA) with fixed-length chromosomes is compared with modified evolutionary methods in which the chromosomes are allowed to vary in size. The results show that, contrary to expectations, the standard UF method performs at least as well as the modified methods, despite the fact that the latter have larger flexibility in exploring the space of possible utility functions. A tentative explanation of the results is given, by means of a simple, analytically tractable, behavioral organization problem.