Organizational-learning oriented Classifier System (OCS) is an extension of Learning Classifier Systems (LCSs) to multiagent environments with introducing the concepts of organizational learning (OL) in organization and management science. Unlike the conventional multiagent systems in the literature, which utilize specific and elaborate techniques, OCS integrates four mechanisms from multi-strategic standpoints. This paper investigates the performance of OCS from the viewpoint of OL and then compares it with conventional LCSs. Intensive experiments on a complex scalable domain have revealed that (1) the integration of four learning mechanisms in OL is effective in solution and computational cost; (2) OCS finds good solutions at less computational cost in comparison with conventional LCSs.
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