Abstract
Many engineering design and developmental activities finally resort to an optimization task which must be solved to get an efficient solution. These optimization problems involve a variety of complexities:
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• Objectives and constraints can be non-linear, non-differentiable and discrete.
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• Objectives and constraints can be non-stationary.
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• Objectives and constraints can be sensitive to parameter uncertainties near the optimum.
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• The number of objectives and constraints can be large.
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• Objectives and constraints can be expensive to compute.
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• Decision or design variables can be of mixed type involving continuous, discrete, Boolean, and permutations.
Currently occupying the Finnish Distinguished Professor position at Helsinki School of Economics, Finland
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Deb, K. (2008). Engineering Optimization Using Evolutionary Algorithms: A Case Study on Hydro-thermal Power Scheduling. In: Hingston, P.F., Barone, L.C., Michalewicz, Z. (eds) Design by Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74111-4_16
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