Titlebook: Applied Multi-objective Optimization; Nilanjan Dey Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive license to S

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Zusammenstellung der benutzten Literatur,objective optimization. These approaches have become more common lately because of their capacity to simultaneously optimize many objectives in a range of areas, including finance, engineering, and healthcare. In a variety of disciplines, including engineering, economics, and medical and environment

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Zusammenstellung der benutzten Literatur,ies a three-part approach: Federated Learning, Counterfactual Explanations and Structural Causal Models to analyse breast cancer gene expression data. First, we use the ability of Federated Learning to train on decentralised data samples, which allows us to gain deep insights into the different gene

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Zusammenstellung der benutzten Literatur,ore than one goal at the same time. These are known as multi-objective optimization problems (MOOPs). Numerous MOOP solutions have been suggested for robotic automation, product design, and other applications. This chapter discusses traditional methods such as scalarization, weighted sum, goal progr

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Zusammenstellung der benutzten Literatur,eously optimize two conflicting objectives: the truss weight and nodal displacement. The Lichtenberg algorithm (LA) draws inspiration from the natural occurrence of radial intracloud lightning and the formation of Lichtenberg figures. It effectively harnesses the fractal nature of these phenomena to

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Zusammenstellung der benutzten Literatur, NP-hard. This method yields a collection of compromise answers rather than a single optimal answer. Feature selection serves as a critical preprocessing phase in machine learning aimed at enhancing the effectiveness of learning strategies by eliminating features unrelated or redundant to the input.

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