Abstract
Real-world optimization problems usually involve multiple objectives to be optimized at the same time under multiple constraints and with reference to many variables, whereas multi-objective optimization itself will be a difficult task, equally tough is that the ability to form a sense of the obtained solutions. This article presents various data mining techniques that can be applied to extract information regarding problems of multi-objective optimization. Further, this knowledge provides a deeper insight into prediction for the decision-makers.
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Raghava Lavanya, D., Niharika, S., Gupta, A.S.A.L.G.G. (2021). Multi-objective Evolutionary Algorithms for Data Mining: A Survey. In: Satapathy, S., Bhateja, V., Janakiramaiah, B., Chen, YW. (eds) Intelligent System Design. Advances in Intelligent Systems and Computing, vol 1171. Springer, Singapore. https://doi.org/10.1007/978-981-15-5400-1_43
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DOI: https://doi.org/10.1007/978-981-15-5400-1_43
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