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| The Gene expression messy genetic algorithm (GEMGA) is a new generation of messy genetic algorithms (GAs) that pays careful attention to linkage learning and in a broader context the search for appropriate relations 6]. This paper revisits the earlier work on the GEMGA 8], 9], 7] and proposes a new version of the algorithm that brings back the time complexity to O((k (`)), wherè is the problem length, is the alphabet set of the representation, and k is the order of delineability 6] of the problem. This paper also reports the scalable linear performance of the GEMGA for various diicult, large optimization problems.