Accelerating Population-Based Search Heuristics By Adaptive Resource Allocation

We investigate a dynamic, adaptive resource allocation scheme with the aim of accelerating the convergence of multi-start population-based search heuristics (PSHs) running on multiple parallel processors. Given that each initialization of a PSH performs differently over time, we develop an exponential learning scheme which allocates computational resources (processors) to each variant in an online manner, based on the performance level attained by each initialization. For the well-known example of (mu + lambda)-evolution strategies, we show that the time required to reach the target quality level of a given optimization problem is significantly reduced and that the utilization of the parallel system is likewise optimized. Our learning approach is easily implementable with currently available batch management systems and provides notable performance improvements without modifying the employed PSH, so it is very well-suited to improve the performance of PSHs in large-scale parallel computing environments.