We present here the basic version of an iterative optimization algorithm that is deterministic, needs just one parameter, and often capable of finding a good solution after very few evaluations of the fitness function. We explain its principles using a multimodal one-dimensional problem. For such problems, it could be applied using nothing more than a ruler and a compass. We provide classical examples illustrating the algorithm’s properties, as well examples where it fails. As this version does not address possible stagnation, it is really only useful for low-dimensional problems (typically at most ten), where each evaluation of a position in the search space is very costly in terms of computational resources.