Quasi-oppositional Differential Evolution
By Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M. A. Salama. IEEE Congress on Evolutionary Computation, 2007
350 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/c4921ab3f725dec21a99175763f6100faaf8f414
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
- Ferrante Neri
- Sedigheh Mahdavi
- Kalyanmoy Deb
- Shahryar Rahnamayan
- Ville Tirronen
- Dawei Du
- D. Simon
- Mehmet Ergezer
In Computational Theory and Mathematics
- Ferrante Neri
- Sedigheh Mahdavi
- Kalyanmoy Deb
- Shahryar Rahnamayan
- Ville Tirronen
- Dawei Du
- D. Simon
- Mehmet Ergezer
Citations in the corpus
- Oppositional biogeography-based optimization (2009, methodology): “To date, OBL has been employed to accelerate the convergence properties of numerous evolutionary algorithms such as differential evolution [140, 141, 142, 143], particle swarm optimization [144, 145, 146, 147], ant colony optimization [148, 149] and simulated annealing [150] in a wide range of field”
- Opposition based learning: A literature review (2017, background): “[209] Quasi-oppositional DE (QODE) was proposed as a uniform random point between the center point and the opposite point.”
- Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “A related algorithm, namely QuasiOppositional Differential Evolution has been presented in Rahnamayan et al. (2007).”