Enhancing the Computational Efficiency of Genetic Programming Through Alternative Floating-Point Primitives

Can evolution operate effectively with noisy floating-point function primitives? In this paper, we are motivated by recent work that aims to accelerate genetic programming (GP) through specialized hardware and field-programmable gate arrays (FPGAs), for which it has been shown that additional performance and power/energy benefits could likely be achieved with floating-point function primitives that trade off enhanced computational efficiency for increased error. Although GP is known to be robust in filtering out certain forms of noise (e.g., within input data), it is not immediately clear that less-accurate function primitives would be viable for GP, since GP formulates arbitrary compositions of its primitives, which could potentially compound error to a prohibitive level. In addition, when introducing more complex forms of computation, such as function differentiation and local optimization techniques, it is not readily apparent that using rougher primitive implementations would be tenable. Here, we address both situations by employing the state-of-the-art CPU-based Operon tool on a diverse set of 15 regression problems, and we show that tree-based GP is capable of evolving very similar (and sometimes better) results with alternative high-performance approximations of standard function primitives, while often also allowing for faster CPU runtimes. Most importantly, in the context of specialized hardware, we conclude that our proposed techniques can likely allow for significant speedups over general-purpose computing platforms, as well as improved power/energy efficiency.