Coevolutionary learning is a flexible paradigm with many applications, but its practice is hindered by various subtle pathologies. The Discovery of Objectives via Clustering (DOC) algorithm is a heuristic approach for learner selection that employs the maximization of expected utility (MEU) solution concept. DOC shows some potential for addressing certain issues; however, modification is found necessary to prevent the pathology of overspecialization. We propose QueMEU, a novel test generator and memory mechanism that uses a sampling policy over a queue to maintain diversity and provide an effective learning gradient. Incorporation of Que- MEU improves the performance of DOC on abstract numbers game problems as well as the challenging density classification task. QueMEU outperforms the standard test generator as well as a more established alternative that employs fitness sharing.

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