Abstract
This paper explores the behavior of the multi–objective neural EDA (MONEDA) in terms of its computational requirements it demands and assesses how it scales when dealing with multi–objective optimization problems with relatively large amounts of objectives. In order to properly comprehend these matters other MOEDAs and MOEAs are included in the analysis. The experiments performed tested the ability of each approach to scalably solve many–objective optimization problems. The fundamental result obtained is that MONEDA is not only yields similar or better solutions when compared with other approaches but also does it with at a lower computational cost.
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Martí, L., García, J., Berlanga, A., Molina, J.M. (2009). On the Computational Properties of the Multi-Objective Neural Estimation of Distribution Algorithm. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008). Studies in Computational Intelligence, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03211-0_20
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DOI: https://doi.org/10.1007/978-3-642-03211-0_20
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