A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II

By Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T. Meyarivan. 2000

5,670 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Real-Time Federated Evolutionary Neural Architecture Search (2020, neutral): “Readers are referred to [45] for more details.”
NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (2018, methodology): “…method shown in Elsken et al. (2018) uses weight sharing through network morphisms Wei et al. (2016b) and approximate morphisms as mutations and uses a biased sampling to select for novelty from the objective space rather than a principled selection scheme like NSGA-II Deb et al. (2000).”
Contemporary Symbolic Regression Methods and their Relative Performance (2021, positive): “Pareto optimization methods [8, 47–49] are popular evolutionary strategies that exploit Pareto dominance relations to drive the population of models towards a set of efﬁcient trade-offs between competing objectives.”
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows (2006, methodology): “In fact, preliminary experiments with the VRPTW used a parameterless diversity strategy from [41], which is an enhancement of the NSGA II strategy [ 40 ].”
A Micro-Genetic Algorithm for Multiobjective Optimization (2001, methodology): “Recent research has shown ways of improving the efficiency of an EMOO technique (see for example [14,6]).”
Infill sampling criteria for surrogate-based optimization with constraint handling (2012, methodology): “(2002) , is a popular method for multiobjective optimization.”
A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization (2003, methodology): “The 2nd niching method employing crowding distance [ 9 ] is also free of choosing such a parameter.”
Indicator-Based Selection in Multiobjective Search (2004, methodology): “To assess the performance values, we have compared the solutions found by the two new algorithms B-IBEA + and B-IBEA HD with NSGA-II [5] and SPEA2 [16].”
A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems (2021, background): “It was initially proposed by Deb et al. [1] in the year 2000 in ‘International Conference on Parallel Problem Solving from Nature .’”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, methodology): “Hence, all algorithms compared inherit the same evolutionary operators as the NSGA-II used in our experiment.”
Novelty-Based Multiobjectivization (2011, positive): “NSGA-II [5], a popular and efficient MOEA, was used to optimize the objectives.”