The bootstrap problem is often recognized as one of the main challenges of evolutionary robotics: if all individuals from the first randomly generated population perform equally poorly, the evolutionary process won't generate any interesting solution. To overcome this lack of fitness gradient, we propose to efficiently explore behaviors until the evolutionary process finds an individual with a non-minimal fitness. To that aim, we introduce an original diversity-preservation mechanism, called behavioral diversity, that relies on a distance between behaviors (instead of genotypes or phenotypes) and multi-objective evolutionary optimization. This approach has been successfully tested and compared to a recently published incremental evolution method (multi-subgoal evolution) on the evolution of a neuro-controller for a light-seeking mobile robot. Results obtained with these two approaches are qualitatively similar although the introduced one is less directed than multi-subgoal evolution.