GDE3: the third evolution step of generalized differential evolution

By Saku Kukkonen, Jouni Lampinen. IEEE Congress on Evolutionary Computation, 2005

710 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0ce74f8c4890a3bff27db91c08f998b6378d0f29

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Citations in the corpus

The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “Besides NSGA-II, jMetal includes the implementation of a number of both classic and modern multi-objective optimizers; some examples are: SPEA2 [27], PAES [26], OMOPSO [29], MOCell [30], AbYSS [31], MOEA/D [32], GDE3 [33], IBEA [34], or SMPSO [35] .”
An efficient multiobjective differential evolution algorithm for engineering design (2009, background): “Such as Pareto DE (PDE) [1], Pareto DE Approach (PDEA) [18], Multiobjective DE (MODE) [28], DE for Multiobjective Optimization (DEMO) [23], GDE3 [14], and -MyDE [24], which uses the -dominance [9] to get a good distribution of solutions retained.”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “…compare the NSGA-II [24], the SPEA2 [110], the (cid:4) -MOEA [67], the (cid:4) -NSGA-II [59], a version of the MOEA/D [105], the IBEA [106], the differential evolution method introduced in [65] (GDE3), the Multi-Objective Particle Swarm Optimizer proposed in [94] (OMOPSO), and the Borg MOEA [36].”
A comparative study of differential evolution variants for global optimization (2006, background): “[9] S. Kukkonen and J. Lampinen.”
A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “In the area of multiobjective CC, good performance was reported in [11] for the ZDT [5] problems where CC was combined with a differential evolution algo-rithm called GDE3 [10].”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, background): “However, as the number of objectives is increased, MOEA/D, GDE3, OMOPSO, and IBEA show signiﬁcant declines in performance.”
Design of microvascular flow networks using multi-objective genetic algorithms (2008, methodology): “Even though the simple GA can be used to solve multi-objective optimization problems (MOOPs) [28], better results can be achieved by using multi-objective evolutionary algorithms (MOEAs) [18,32,12, 21 ].”
Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution (2010, background): “Generalized differential evolution (GDE3) [55] is a multiobjective DE algorithm that has”
Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, neutral): “For example, the CCGDE3 [19] randomly divides the decision variables into several groups of equal size, then optimizes each group of decision variables by GDE3 [20].”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “To deal with the shortcomings of GDE2 regarding slow convergence, Kukkonen and Lampinen proposed an improved version called GDE3 [35] (a combination of the earlier GDE versions and the Pareto-based differential evolution algorithm [S67]).”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, neutral): “gdE3 [64] 2005 gEnERAliZEd diffEREnTiAl EvoluTion 3”
Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, background): “In addition to the use of constraint violation, the number of violated constraints [28] and the dominance relation based on constraints [29], [30] have also been considered.”
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.”
jMetal: A Java framework for multi-objective optimization (2011, background): “Implementation of a number of modern multi-objective optimization algorithms: NSGA-II [6], SPEA2 [8], PAES [7], PESA-II [13], OMOPSO [14], MOCell [15], AbYSS [16], MOEA/D [17], Densea [18], CellDE [19], GDE3 [20], FastPGA [21], IBEA [22], SMPSO [23], MOCHC [24], and SMS-EMOA [25].”