Solving Multiobjective Optimization Problems Using an Artificial Immune System

By Carlos Artemio Coello Coello, Nareli Cruz-Cortés. Genetic Programming and Evolvable Machines, 2005

1,012 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/27c9fed3e038f37805962f5f9db3b6f594034bd8

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Citations in the corpus

Performance metrics in multi-objective optimization (2015, background): “8 Coverage [21] 9 Coverage of the front [22] 10 D metric [23] D 11 D quantifier [24] 12 D1R [25] 13 Distance-based indicator [26] D(p) 14 Diversity metric [15] DM 15 Dominance-based quality [27] DQp 16 Entropy metric [28] 17 Epsilon family [2] 18 Generational distance [12] GD 19 Hypervolume [13] HV ”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “He and Hui [215], DE – Das et al. [2], PSO – Sun et al. [225], Du et al. [229], Thangavel et al. [236], AIS – Lie et al. [260], Review – Jiang et al. [30], Lukashin et al. [31], Xu and Wunsch [91], Hruschka et al. [3,119,120], Jain et al. [34], MODE – Suresh et al. [343] Intrusion detection GA – Liu”
Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection (2008, methodology): “In this section, we compare PESA-II (Corne et al., 2001), NSGA-II (Deb et al., 2002a), SPEA2 (Zitzler et al., 2002), and MISA (Coello Coello and Cortes, 2005) with NNIA in solving thirteen well-known multiobjective function optimization problems including three low-dimensional bi-objective…”
A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, methodology): “These cloning and mutation processes are termed the clonal selection principle [200], which is one of the inspiring methodologies employed in AIS for solving optimization problems.”
Performance indicators in multiobjective optimization (2018, background): “1 3 3 3 3 3 Inverted generational distance [23] 3.”
Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization (2012, background): “1) Discussion of the Original Indicator: Analog to GD, the indicator IGD as proposed in [5] can be written as follows:”