This study is the second using real-coded representation for problems usually solved with a discrete coding. The adaptive generative representation is able to adapt itself on the fly to prior parts of the construction of an object as it assembles it. In the initial study the ability of the representation to take user supplied or problem supplied biases that change its behavior was demonstrated but not explored. In this study the bias is used to change the way evolution explores a fitness landscape for both an RFID antenna design problem and small instances of the traveling salesman problem. Addition of a bias to two different generative representations promotes the evolution of longer antenna designs (a heuristic objective associated with good antennas) while leading the algorithm to generate designs with distinctive shape characteristics. For the traveling salesman, a simple inverse-distance bias for the adaptive generative representation causes a large improvement in performance over a random key representation in 99 of 100 instances studied.
