Genetic algorithm for multi objective optimization
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Abstract
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints.
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Polupan, V. Genetic algorithm for multi objective optimization / V. Polupan, G. Cherednichenko // Food Science for Well-being (CEFood 2016) : 8th Central European Congress on Food, 23–26 May 2016. – Kyiv : NUFT, 2016. – P. 105.