We introduce a C.G. constraint on adaptive random testing (ART) for programs with numerical input. One rationale behind adaptive random testing is to have the test candidates to be as widespread over the input domain as possible. However, the computation may be quite expensive in some cases. The C.G. constraint is introduced to maintain the widespreadness while reducing the computation requirement in terms of number of distance measures. Three variations of C.G. constraints and their performance when compared with ART are discussed.