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- W2053313404 abstract "Evolutionary multi-objective optimization (EMO) methodologies, suggested in the beginning of Nineties, focussed on the task of finding a set of well-converged and well-distributed set of solutions using evolutionary optimization principles. Of the EMO methodologies, the elitist non-dominated sorting genetic algorithm or NSGA-II, suggested in 2000, is now probably the most popularly used EMO procedure. NSGA-II follows three independent principles -- domination principle, diversity preservation principle and elite preserving principle -- which make NSGA-II a flexible and robust EMO procedure in the sense of solving various multi-objective optimization problems using a common framework. In this paper, we describe NSGA-II through a functional decomposition following the implementation of these three principles and demonstrate how various multi-objective optimization tasks can be achieved by simply modifying one of the three principles. We argue that such a functionally decomposed and modular implementation of NSGA-II is probably the reason for it's popularity and robustness in solving various types of multi-objective optimization problems." @default.
- W2053313404 created "2016-06-24" @default.
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- W2053313404 date "2008-07-12" @default.
- W2053313404 modified "2023-10-16" @default.
- W2053313404 title "A robust evolutionary framework for multi-objective optimization" @default.
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- W2053313404 doi "https://doi.org/10.1145/1389095.1389223" @default.
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