A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling
By Ran Cheng, Yaochu Jin, Kaname Narukawa, Bernhard Sendhoff. IEEE Transactions on Evolutionary Computation, 2015
330 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/2becde8f53c89bac6c3ccfd832d45ef8196593f1
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Citations in the corpus
- Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, methodology): “The general design principle of the linkage functions is similar to that in [30] and [31], where each decision variable in x s (denoted as x si , i = 1 , . . . , n s ) is linked with the first decision variable in x f , i.e., x f 1 .”
- An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, background): “Note that all of the proposed MOPs are with real variables, so the MOEAs using specific operators (e.g., particle swarm optimization [65], differential evolution [66], and estimation of distribution algorithm [67]) can be tested on them.”
- An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “modeling [17], NSGA-III [18], and SPEA based on reference direction [19], decompose an MOP into several simpler MOPs by partitioning the objective space into a number of subspaces.”
- Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, background): “The image of all the Pareto optimal solutions in the objective space is said to constitute the Pareto front (PF) [30].”
- A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “In order to generate uniformly distributed reference vectors, we adopt the approach introduced in [40].”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, background): “This is due to the fact that the grids in the grid dominance uniformly distribute in an M-dimensional hypercube, whereas the Pareto front is an (M − 1)-dimensional manifold [37], and thus the grids intersecting with the Pareto front are discrete.”
- Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “In terms of the Wilcoxon rank sum test, the proportion of test instances where LMOCSO performs significantly better than WOF-NSGA-II, LMEA, MMOPSO, and IM-MOEA is 21/27, 24/27, 24/27 and 22/27, respectively.”
- PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, background): “f1–f10 foR im-moEA [71] 2015 ThE TEST PRoblEmS dESignEd foR im-moEA”
- A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization (2018, methodology): “To generate a uniformly distributed set of reference vectors, first a set of uniformly distributed reference points (p) is generated on a unit hyperplane using the canonical simplexlattice design method [7], [11]”
- Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization (2020, methodology): “Following the design principles in [52]–[54], we will adopt”