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We can think of a cognitive radio as having three basic parts that make it cognitive: the ability to sense, including at a minimum sensing the RF spectrum, geographical surroundings, and the user’s needs; the capacity to learn, ideally in both supervised and unsupervised modes; and finally, the capability to adapt within any layer of the radio communication system. At the Virginia Tech (VT) Center for Wireless Telecommunications (CWT), we have developed a cognitive radio engine to perform all of these tasks. This paper presents the adaptive component, which uses genetic algorithms (GAs) to evolve a radio defined by a chromosome. The chromosome’s genes represent the adjustable parameters in a given radio, and by genetically manipulating the chromosomes, the GA can find a set of parameters that optimize the radio for the user’s current needs. At the end of this paper, we present experimental results on both a hardware platform and software simulation.