Low-level representations have proven to be good at certain kinds of adaptive learning. High-level representations make effective use of existing knowledge and perform inference well. To promote using both forms of representation cooperatively rather than engaging in the perennial sectarian debate of supporting one paradigm at the expense of the other, this thesis presents a prototype system demonstrating knowledge retention using genetic algorithms and multiple levels of representation and learning. The prototype uses a mid-level of representation and transformations upward and downward for retaining domain-specific knowledge to bridge the gap between the high-level representation and learning and the genetic algorithm level. The thesis begins with an overview of the work, briefly introduces the principles of genetic algorithms, and states an illustrative domain. Then it reviews related work and two supportive systems. After that, it gives a general description of the prototype system's structure, three levels of representation, two transformations, and three levels of learning. Next, it describes methods of implementing the prototype system in some detail. Finally, it shows results with discussion, and points out conclusions and future work. / Master of Science