Encodings and genetic operators for efficient evolutionary design of digital circuits

摘要

The performance of evolutionary computation-based methods, such as genetic algorithms, depends primarily on the fitness function. Nonetheless, they are also critically impacted by the choice of the representation of potential solutions and used genetic operators. With this motivation, we analyse the impact of two different circuit encodings used for circuit evolution on the process performance. The main objective is to elect the more appropriate encoding and genetic operators to yield efficient designs regarding the cost function. The first approach is based on genetic programming while the second views circuits as gate maps. Furthermore, we compare the impact of both circuit representations on the evolutionary process. Evolved circuits must implement the exact expected behaviour, yet, they should minimise hardware and time requirements. We show that for the same input/output behaviour, employing both approaches yields circuits of similar characteristics. However, the evolution process is more efficient in the case the second encoding.