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Engineering Optimization is an interdisciplinary journal that serves a vast technical community interested in quantitative computational methods of optimization. It emphasizes their practical applications across engineering disciplines including planning, design, manufacturing, and operational processes. The journal's policy considers optimization as any formal numerical process aimed at enhancing technical performance and fostering innovation within engineering domains. While its primary focus lies on algorithms for numerical optimization, the journal also encourages submissions utilizing methodologies from operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, generative artificial intelligence, machine/deep learning, information theory, quantum computing, quantum information processing, and other cutting-edge techniques relevant to optimization in decision-making processes. For example, within the realm of artificial intelligence, the journal welcomes academic papers that explore how artificial intelligence methodologies enhance numerical optimization algorithms or propose innovative artificial intelligence methods to solve engineering optimization challenges.
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