Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets
暂无分享,去创建一个
[1] Lucas Bradstreet,et al. A Fast Incremental Hypervolume Algorithm , 2008, IEEE Transactions on Evolutionary Computation.
[2] Marco Laumanns,et al. Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[3] Nicola Beume,et al. S-Metric Calculation by Considering Dominated Hypervolume as Klee's Measure Problem , 2009, Evolutionary Computation.
[4] Enrique Alba,et al. The jMetal framework for multi-objective optimization: Design and architecture , 2010, IEEE Congress on Evolutionary Computation.
[5] Lothar Thiele,et al. Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.
[6] Jie Zhang,et al. A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm , 2015, IEEE Transactions on Cybernetics.
[7] M. L. Fisher,et al. An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..
[8] Antonio J. Nebro,et al. jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..
[9] Carlos M. Fonseca,et al. Hypervolume Subset Selection in Two Dimensions: Formulations and Algorithms , 2016, Evolutionary Computation.
[10] Mark H. Overmars,et al. New upper bounds in Klee's measure problem , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.
[11] Tobias Friedrich,et al. Approximating the least hypervolume contributor: NP-hard in general, but fast in practice , 2008, Theor. Comput. Sci..
[12] Michel Minoux,et al. Accelerated greedy algorithms for maximizing submodular set functions , 1978 .
[13] Xin Yao,et al. An Empirical Investigation of the Optimality and Monotonicity Properties of Multiobjective Archiving Methods , 2019, EMO.
[14] Joshua D. Knowles,et al. Bounded archiving using the lebesgue measure , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[15] Tobias Friedrich,et al. An Efficient Algorithm for Computing Hypervolume Contributions , 2010, Evolutionary Computation.
[16] Lothar Thiele,et al. Bounding the Effectiveness of Hypervolume-Based (μ + λ)-Archiving Algorithms , 2012, LION.
[17] Hisao Ishibuchi,et al. How to compare many-objective algorithms under different settings of population and archive sizes , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).
[18] Tobias Friedrich,et al. Generic Postprocessing via Subset Selection for Hypervolume and Epsilon-Indicator , 2014, PPSN.
[19] Tapabrata Ray,et al. Distance-Based Subset Selection for Benchmarking in Evolutionary Multi/Many-Objective Optimization , 2019, IEEE Transactions on Evolutionary Computation.
[20] Tobias Friedrich,et al. Approximating the volume of unions and intersections of high-dimensional geometric objects , 2008, Comput. Geom..
[21] Eckart Zitzler,et al. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.
[22] R. Lyndon While,et al. Improving the IWFG algorithm for calculating incremental hypervolume , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).
[23] Lucas Bradstreet,et al. A Fast Way of Calculating Exact Hypervolumes , 2012, IEEE Transactions on Evolutionary Computation.
[24] Hisao Ishibuchi,et al. Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios , 2017, IEEE Access.
[25] Karl Bringmann,et al. Two-dimensional subset selection for hypervolume and epsilon-indicator , 2014, GECCO.
[26] Kalyanmoy Deb,et al. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.
[27] Carlos M. Fonseca,et al. Computing and Updating Hypervolume Contributions in Up to Four Dimensions , 2018, IEEE Transactions on Evolutionary Computation.
[28] Andreas Krause,et al. Cost-effective outbreak detection in networks , 2007, KDD '07.