We have presented an empirical model of genetic algorithms (GA) that puts parallel genetic operators in a cooperative-competitive stand with each other. An improved GA (GA-SRM) based on this model remarkably improves the search performance of a single population GA. We extend GA-SRM to distributed GAs in order to improve the performance of multiple population GAs. Simulation results verify that the parallel genetic operators in GA-SRM, CM and SRM, can successfully contribute to improve the search performance of distributed GAs.

We extend an improved GA (GA-SRM) to the multi-objective flowshop scheduling problem (FSP) in order to obtain better pareto-optimum solutions (POS). Two kinds of cooperative-competitive genetic operators in GA-SRM, CM and SRM, are extended to ones suitable for FSP in which solutions (individuals) are represented as permutations. Simulation results verify that GA-SRM shows better performance for the multi-objective optimization problem (MOP), and consequently better POS are obtained than conventional approaches with canonical GA.

We interpret Reed-Muller codes in terms of superimposition and present a new decoding algorithm for Reed-Muller codes. Before presenting this algorithm, we propose a decoding algorithm for a class of simple iterated codes (SI codes) that will play an important role in our new decoding algorithm. Finally, we compare our algorithm with the conventional algorithm for the cyclic Reed-Muller codes from the standpoint of decoding delay.