The growing complexity of systems and their implementation into silicon encourages designers to look for model designs at higher levels of abstraction and then incrementally build ...
We present a molecular computing algorithm for evolving DNA-encoded genetic programs in a test tube. The use of synthetic DNA molecules combined with biochemical techniques for va...
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Thermal effects are becoming increasingly important during integrated circuit design. Thermal characteristics influence reliability, power consumption, cooling costs, and performan...
Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept ...