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Status: Unchecked ×Keyword: NSGA-II ×Clear all

3 claims from 2 papers

1. Computer Science › Advanced Multi-Objective Optimization Algorithms

   A fast and elitist multiobjective genetic algorithm: NSGA-II

   Deb, Pratap, Agarwal and Meyarivan · IEEE Transactions on Evolutionary Computation · 2002

   The paper proposes NSGA-II, a multi-objective genetic algorithm that tackles three criticisms of earlier methods: high computational cost, lack of elitism and the need for a sharing parameter.

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   1. UncheckedThe paper presents a fast non-dominated sorting method whose computing cost grows as O(MN²), where M is the number of objectives and N the population size.“Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented.”
   2. UncheckedIn simulations, a constrained version of NSGA-II performed much better than another constrained multi-objective optimizer on several test problems.“Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.”

2. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   An integrative approach to protein sequence design through multiobjective optimization

   Hong and Kortemme · PLoS Computational Biology · 2024

   The authors adapt an evolutionary multiobjective optimisation method (NSGA-II) that combines several protein models to design sequences, and test it on RfaH, PapD and calmodulin.

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   1. UncheckedFor the fold-switching protein RfaH, a multiobjective evolutionary design method recovered the native sequence with less bias and variance than ProteinMPNN used directly.“Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in Rf…”