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The growing interest in grids technologies for the solving of large-scale computational problems leads related framework improvement. One of the challenging problems in Grid computing is the efficient resources utilization and allocation of tasks, i.e. scheduling problem. Fuzzy Rule-Based Systems (FRBSs) have recently proved to be a competitive alternative for the development of scheduling systems, outperforming extensively used scheduling strategies such as EASY Backfilling or Greedy. However, FRBSs-based schedulers performance strongly depends on their data bases quality and a major effort is still required for the knowledge acquisition process improvement. This paper presents a fuzzy rule-based meta-scheduler incorporating a new genetic approach for the learning process. Concretely, the suggested learning strategy is inspired by classical rule evolution strategies, Pittsburgh and Michigan approaches. Experimental results show that further accuracy in the learning process of fuzzy meta-schedulers can be achieved without significantly increasing the associated computational effort.