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- W2023921780 abstract "Real-time systems have to react to disruptive events in manufacturing environments within tight time constraints. This article aims to enhance standard genetic algorithms (GA) to reduce the execution time and improve the solution quality when the objective function is time-expensive to evaluate. More precisely, we propose the addition of a cache memory to avoid re-testing an already tested solution and a predator mechanism to relax evaluation. After discussing the applicability conditions of those mechanisms, we propose a demonstrative example, based on a real scenario in the aluminium industry, in which a GA and a real-time discrete event simulation model are integrated to support the rescheduling process in enterprise resource planning controlled environments." @default.
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- W2023921780 date "2011-02-10" @default.
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- W2023921780 title "An improved genetic algorithm approach for on-line optimisation problems" @default.
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- W2023921780 doi "https://doi.org/10.1080/09537287.2010.543556" @default.
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