Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion
By Aimin Zhou, Yaochu Jin, Qingfu Zhang, Bernhard Sendhoff, Edward P. K. Tsang. International Conference on Evolutionary Computation, 2006
343 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/a66776c5c07f6cc7391e320efca1013185ea6a52
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Citations in the corpus
- Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization (2014, background): “[43] introduced the generalized spread ( ∗) as an extension of , which takes the form”
- Performance metrics in multi-objective optimization (2015, background): “42 Spread: Generalized Spread [43] Δ∗ 43 Success Counting [44] SCC 44 Sum of maximum objective values [45] Smax 45 The adjusted Rand Index [46] 46 The average distance of points to the PF [47] 47 The delineation metric [48] 48 The diversity metric [48] 49 The front Spread [49] FS 50 The front to Set”
- AbYSS: Adapting Scatter Search to Multiobjective Optimization (2008, background): “This modification is based on the metric proposed in [35] (2) where is a set of solutions, is the set of Pareto optimal solutions, are extreme solutions in , is the number of objectives, and (3) (4) TABLE II SUMMARY OF THE DIFFERENT CONFIGURATIONS USED IN ABYSS If the achieved solutions are well…”
- A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, methodology): “In this paper, we use the generalized SPREAD (denoted by ) instead, which was first proposed in [54] (P) = ∑m i=1 dist(ei, S) + ∑ z∈S |dist(z, S) − d| ∑m i=1 dist(ei, S) + |S| ∗ d (8) where S is a set of solutions, S∗ is the set of Pareto-optimal solutions, e1, e2, . . . , em are m extreme solutions”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “For calculating IGD, roughly 10 000 reference points on the Pareto front of each test instance are sampled by the Das and Dennis’s approach with two layers.”
- Recent Advances in Bayesian Optimization (2022, background): “Various quality indicators have been proposed, including inverted generational distance [216] and hypervolume (HV) [220].”
- An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “4) Performance metrics: The inverted generational distance (IGD) [31] and the hypervolume (HV) [32] are adopted to measure the solution sets in terms of both convergence and diversity quality.”
- A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, methodology): “The inverted generational distance (IGD) metric [48, 51] is adopted to evaluate the performance of the compared algorithms, and roughly 5000 points uniformly sampled on the Pareto fronts are used in the calculation of IGD for each test problem.”
- Many-Objective Evolutionary Algorithms (2015, background): “The generalized spread ( I GS ) [Zhou et al. 2006], which measures the diversity of an approximation set, is defined as follows: U is a utility function set [Hansen and Jaszkiewicz 1998; Brockhoff et al. 2012].”
- Improved Regularity Model-Based EDA for Many-Objective Optimization (2018, background): “regularity-based EDAs for MOPs [20], [37], [38], an improved”
- jMetal: A Java framework for multi-objective optimization (2011, methodology): “This extension was based on the metric proposed in [47]:”
- Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “Performance metric: The inverted generational distance (IGD) [61] and hypervolume (HV) [62] are adopted to assess the performance of MOEAs in the experiment.”
- PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, background): “In addition, there are a lot of performance indicators provided by PlatEMO for experimental studies, including Coverage [71], generational distance (GD) [85], hypervolume (HV) [86], inverted generational distance (IGD) [87], normalized hypervolume (NHV) [37], pure diversity (PD) [88], Spacing [89], ”
- Performance indicators in multiobjective optimization (2018, background): “Delta indexes (∆′, ∆ and ∆) [22, 27] Deb [22] introduces the ∆′ index for biobjective problems”