Running performance metrics for evolutionary multi-objective optimizations

By Kalyanmoy Deb, S. Jain. 2002

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3a27ac23c1248ccc32d99ba87e33c9ceea9c10b4

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Citations in the corpus

Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization (2014, methodology): “2The results are evaluated by the convergence measure (CM) metric [18].”
Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection (2008, methodology): “Figure 13 and Figure 14 show the mean values of the normalized Convergence metric (Deb and Jain, 2002) and Spacing over 30 independent runs in 1 to 1000 generations when NNIA solves DTLZ1 and DTLZ3 with 2, 3, 5, 7 and 9 objectives, respectively.”
Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design (2005, methodology): “Other relevant parameters include a diversity metric grid specification of 12 blocks for each coordinate axis of the projected solutions (see Deb and Jain [11] for additional information) and e resolutions settings (ecost, econc, euncert, and emass) for the e-performance metric of 100, 10 1, 100, an”
A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, result): “the diversity maintenance mechanism obtains even better convergence metric [24] results than the original NSGA-II on the 5-objective DTLZ2 problem.”
On the Evolutionary Optimization of Many Conflicting Objectives (2007, methodology): “Following the approach of Deb and Jain [31], a functional approach is taken in this paper, in which specific unary indicators are”
Evaluating the -Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions (2005, result): “Instead, we define a sparsity measure, which is similar to the entropy measure (Farhang-Mehr and Azarm, 2002) or the grid diversity measure (Deb and Jain, 2002) introduced elsewhere.”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, methodology): “Commonly used metrics measuring solution convergence of multi-objective algorithms are: (i) the hypervolume metric (Zitzler, 1999) or its variants, which calculate the hypervolume of the region dominated by a set of solutions (e.g. Kollat et al., 2008; Kollat and Reed, 2006; Reed et al., 2013; Zitzl”
Performance Scaling of Multi-objective Evolutionary Algorithms (2003, background): “Performance Metrics • Functionally independent [7] set of variables – Convergence to the reference set – Diversity – Running time”
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, methodology): “In our study, two widely-used quality metrics, convergence measure (CM) and diversity measure (DM) [14], are selected.”
Performance metrics in multi-objective optimization (2015, methodology): “Two types of metrics have been used in the specialized literature: • Unary metrics: The metric is said to be unary if it receives as parameter only one approximation set A to be evaluated.”