A Hardware Genetic Algorithm for the Travelling Salesman Problem on SPLASH 2

With the introduction of Splash, Splash 2, PAM, and other reconngurable computers, a wide variety of algorithms can now be feasibly constructed in hardware. In this paper, we describe the Splash 2 Parallel Genetic Algorithm (SPGA), which is a parallel genetic algorithm for optimizing symmetric traveling salesman problems (TSPs) using Splash 2. Each processor in SPGA consists of four Field Pro-grammable Gate Arrays (FPGAs) and associated memories and was found to perform 6.8 to 10.6 times the speed of equivalent software on a state-of-the-art workstation. Multiple processor SPGA systems, which use up to eight processors, nd good TSP solutions much more quickly than single processor and software-based implementations of the genetic algorithm. The four-processor island-parallel SPGA implementation out performed all other SPGA conngurations tested. We conclude noting that the described parallel genetic algorithm appears to be a good match for reconngurable computing machines and that Splash 2's various interconnect resources and support for linear systolic and MIMD computing models was important for the implementation of SPGA.