Constraint-handling in nature-inspired numerical optimization: Past, present and future
By Efrén Mezura-Montes, Carlos Artemio Coello Coello. Swarm and Evolutionary Computation, 2011
455 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/f98cb6435e26c47e7600b696ac1cd2787846574d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Anne Auger
- Ryoji Tanabe
- Nikolaus Hansen
- Hisao Ishibuchi
- Dirk V. Arnold
- J. Urgen Schmidhuber
- Christian Igel
- Yaochu Jin
In Computational Theory and Mathematics
- Anne Auger
- Nikolaus Hansen
- Hisao Ishibuchi
- Ryoji Tanabe
- Ponnuthurai Nagaratnam Suganthan
- Yaochu Jin
- Francisco Herrera
- Frank Hutter
Citations in the corpus
- Evolution Strategies (2018, background): “We mention here constraint handling [48, 49], fitness surrogates [50], multi-objective variants [51, 52], and exploitation of fitness values [53].”
- Differential evolution with multi-population based ensemble of mutation strategies (2016, unknown): “rules, stochastic ranking and ε-constrained method [34].”
- An easy-to-use real-world multi-objective optimization problem suite (2020, background): “Studies on unconstrained multi-objective optimization problems can be the basis of studies on more challenging problems, including constrained multiobjective optimization problems [34], dynamic multi-objective optimization problems [35], and multi-modal multi-objective optimization problems [36].”