A partially asynchronous global parallel genetic algorithm
Darren M. Chitty · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2021
Population-based meta-heuristics such as Genetic Algorithms (GA) are ideal for exploiting multiple processor cores. With parallel architectures now standard computationally intensive methods need to harness them to best effect. A synchronous globally parallel GA creates and evaluates population members in parallel at each generation resulting in considerable processor time spent waiting for threads. An asynchronous approach whereby parallel threads continue evolution without waiting addresses this issue but can result in memory conflicts. This paper introduces an asynchronous global GA model for shared memory CPUs without memory conflicts. Experiments demonstrate performance gains of 1.35 to 12 fold dependant on problem and population sizes. However, an asynchronous model leads to non-uniform evolution reducing accuracy. Consequently, this paper demonstrates that combining synchronous and asynchronous methods into a partially asynchronous model retains a speed advantage whilst improving solution accuracy.