Using explicit averaging fitness for studying the behaviour of rats in a maze

In this paper we study the performance of different evolutionary strategies based on explicit averaging. On a previous study, (Costa et al., 2012) proposed a probabilistic fitness function for an agent model based on neural networks and genetic algorithms employed to investigate the behaviour of rats in an elevated plus-maze (EPM). Differently from other computational models, the virtual rat proposed in (Costa et al., 2012) is not built based on experimental data comparisons with real rats, but, instead, is based on a behavioural model exploring the conflict between fear and anxiety. Despite the good results of the proposed agent, the effects of the uncertain fitness functions in the evolutionary learning process were not studied in the previous study. In our experiments we found significant differences in the performance of the genetic algorithm when the fitness of the individuals is sampled different times thus enabling us to define the best strategy for the studied problem. Genetic algorithm, Uncertainty, Explicit averaging fitness, Elevated plus-maze, Rat