Distributed scheduling with decomposed optimization criterion: Genetic programming approach

Abstract

A new approach to develop parallel and distributed scheduling algorithms for multiprocessor systems is proposed. Its main innovation ties in evolving a decomposition of the global optimization criteria. For this purpose a program graph is interpreted as a multi-agent system. A game-theoretic model of interaction between agents is applied. Competetive coevolutionary genetic algorithm, termed loosely coupled genetic algorithm, is used to implement the multi-agent system. To make the algoritm trully distributed, decomposition of the global optimization criterion into local criteria is proposed. This decomposition is evolved with genetic programming. Results of succesive experimental study of the proposed algorithm are presented.