Evolution of complex autonomous robot behaviors using competitive fitness

Evolutionary robotics (ER) employs population-based artificial evolution to develop behavioral robotics controllers. We focus on the formulation and application of a fitness selection function for ER that makes use of intra-population competitive selection. In the case of behavioral tasks, such as game playing, intra-population competition can lead to the evolution of complex behaviors. In order for this competition to be realized, the fitness of competing controllers must be based mainly on the aggregate success or failure to complete an overall task. However, because initial controller populations are often subminimally competent, and individuals are unable to complete the overall competitive task at all, no selective pressure can be generated at the onset of evolution (the bootstrap problem). In order to accommodate these conflicting elements in selection, we formulate a bimodal fitness selection function. This function accommodates subminimally competent initial populations in early evolution, but allows for binary success/failure competitive selection of controllers that have evolved to perform at a basic level. Large arbitrarily connected neural network-based robot controllers were evolved to play the competitive team game Capture the Flag. Results show that neural controllers evolved under a variety of conditions were competitive with a hand-coded knowledge-based controller and could win a modest majority of games in a large tournament.