Abstract
In the previous chapter, we have shown empirically that the performance of MOEA deteriorates quickly with increasing noise intensities. As the results suggest, the canonical MOEA will face difficulties identifying non-dominated solutions, let alone maintaing a diverse set of near-optimal solutions.
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© 2009 Springer-Verlag Berlin Heidelberg
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Goh, CK., Tan, K.C. (2009). Handling Noise in Evolutionary Multi-objective Optimization. In: Evolutionary Multi-objective Optimization in Uncertain Environments. Studies in Computational Intelligence, vol 186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-95976-2_3
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DOI: https://doi.org/10.1007/978-3-540-95976-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-95975-5
Online ISBN: 978-3-540-95976-2
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