A coevolutionary approach to cellular automata-based task scheduling

Abstract

Cellular Automata (CA) have been proposed for task scheduling in multiprocessor architectures. CA-based models aim to be fast and decentralized schedulers. Previous models employ an off-line learning stage in which an evolutionary method is used to discover cellular automata rules able to solve an instance of a task scheduling. A central point of CA-based scheduling is the reuse of transition rules learned for one specific program graph in the schedule of new instances. However, our investigation about previous models showed that evolved rules do not actually have such generalization ability. A new approach is presented here named multigraph coevolutionary learning, in which a population of program graphs is evolved simultaneously with rules population leading to more generalized transition rules. Results obtained have shown the evolution of rules with better generalization abilitywhen they are compared with those obtained using previous approaches. © 2012 Springer-Verlag Berlin Heidelberg.