The Planning and Scheduling (PS) problem plays a vital role in several domains, such as economics, military, management, finance, and games, where finding the optimal plan and schedule to achieve specific goals is essential. In this article, we present a Genetic Algorithm for the Planning and Scheduling (GAPS) problem in the StarCraft II Build Order Optimization problem (SC2 BO) context -- as it signifies that modern strategy games present a more challenging environment than classical planning problems. We evaluate the performance of GAPS and compare it with state-of-the-art methods. Experimental results provide valuable insight into the effectiveness of GA in the context of the PS Problem under various configurations, notably in the context of Lamarckianism and the Baldwin Effect. Ultimately, this research enhances the understanding of GA application for the PS problem, offering notable insights regarding GA performance and potential for future work.
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