Titlebook: Evolutionary Multi-objective Optimization in Uncertain Environments; Issues and Algorithm Chi-Keong Goh,Kay Chen Tan Book 2009 Springer-Ver

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https://doi.org/10.1007/978-3-663-07215-7lutionary optimization has led to the development of evolutionary artificial neural networks (EANN) in which adaptation is performed primarily by means of evolution. Given that the intrinsic relationship between the architecture and the associated synaptic weights can be quite complex, the design me

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Grundlegende Auswertung der Messergebnisse,blems involving such dynamic systems, the fitness landscape changes to reflect the time-varying requirements of the systems. Examples of such problems can be found in the areas of control, scheduling, vehicle routing, and autonomous path planning.

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Unternehmen als Institution und Organisationptimization considers the effects explicitly and seeks to minimize the consequences without eliminating efficiency. Many different approaches, including Taguchi orthogonal arrays, response surface methodology, probabilistic design analysis, have been applied for robust optimization. In operational r

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that when some data are random, it is no longer possible to require that all constraints be satisfied for all realizations of the random variables (170). In addition, the actual cost of a particular solution to the VRPSD cannot be known with certainty before the actual implementation of the solution

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Experimenting with AVR Microcontrollersn with uncertainties. This goal has been accomplished through a relatively easy walk-through on the basic ideas found in the single-objective literature that can be applied to handle multi-objective noisy, dynamic and robust fitness landscapes. These ideas have been introduced in the context of unce