We present a method of generating encryptors, in particular, Pseudo Ran- dom Number Generators (PRNG), using evolutionary computing. Working with a sys- tem called Eureqa ,d esigned by the Cornell Creative Machines Lab, we seed the system with natural noise sources obtained from data that can include atmospheric noise gen- erated by radio emissions due to lightening, for example, radioactive decay, electronic noise and so on. The purpose of this is to 'force' the system to output a result (a non- linear function) that is an approximation to the input noise. This output is then treated as an iterated function which is subjected to a range of tests to check for potential cryp- tographic strength in terms of a positive Lyapunov exponent, maximum entropy, high cycle length, key di↵usion characteristics etc. This approach provides the potential for generating an unlimited number of unique PRNG that can be used on a 1-to-1 basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalised encryption algorithm rather than just a personal key using a 'known algorithm' that may be subject to attack and/or is 'open' to the very authorities who are promoting its use.