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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Evolving Robust Solutions in Multi-Objective Optimization tractability, resulting in more uncertainties in the problem model. In addition, it does not allow for the incorporation of any domain knowledge to achieve better performance. On the other hand, evolutionary optimization techniques do not have such limitations, making it appropriate for robust optimization.

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Noisy Evolutionary Multi-objective Optimization external sources, noise can also be intrinsic to the problem. A good example is the evolution of neural networks where the same network structure can give rise to different fitness values due to different weight instantiations [144].

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1860-949X y algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in t

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https://doi.org/10.1007/978-3-663-07215-7s of evolution. Given that the intrinsic relationship between the architecture and the associated synaptic weights can be quite complex, the design methodology would be flawed if we were to decouple these two properties during the training phase of the network. The design of ANN has two intrinsical noise sources:

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https://doi.org/10.1007/978-3-658-01120-8n process, which inevitably leads to the inability to track the dynamic Pareto front. Therefore, it is necessary to maintain or generate sufficient diversity to explore the search space when the multi-objective problem changes.