Gray-Box Optimization using the Parameter-less Population Pyramid
Brian W. Goldman, William F. Punch · 2015
Unlike black-box optimization problems, gray-box optimization problems have known, limited, non-linear relationships between variables. Though more restrictive, gray-box problems include many real-world applications in network security, computational biology, VLSI design, and statistical physics. Leveraging these restrictions, the Hamming-Ball Hill Climber (HBHC) can efficiently find high quality local optima. We show how 1) a simple memetic algorithm in conjunction with HBHC can find global optima for some gray-box problems and 2) a gray-box version of the Parameter-less Population Pyramid (P3), utilizing both the HBHC and the known information about variable relationships, outperforms all of the examined algorithms. While HBHC's inclusion into P3 adds a parameter, we show experimentally it can be fixed to 1 without adversely effecting search.