Stochastic Adaptive Search Methods: Theory and Implementation
By Zelda B. Zabinsky. 2015
17 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Optimization & Training
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
- Swagatam Das
- Ponnuthurai Nagaratnam Suganthan
- Seyedali Mirjalili
- Simon Lacoste-Julien
- Francisco Herrera
- Xin-She Yang
- Seyed Mohammad Mirjalili
- Pinar Çivicioglu
In Computer Networks and Communications
- Simon Lacoste-Julien
- Andrea Lodi
- David Camacho
- Carlos Artemio Coello Coello
- Ponnuthurai Nagaratnam Suganthan
- Francisco Herrera
- Swagatam Das
- Yoshua Bengio
In Optimization & Training
- Simon Lacoste-Julien
- Sébastien Lachapelle
- Yoshua Bengio
- Ponnuthurai Nagaratnam Suganthan
- Andrea Lodi
- Carlos Artemio Coello Coello
- Francisco Herrera
- Swagatam Das
Citations in the corpus
- Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information (2018, background): “…to solve (1) and (2) include response surface methods (internal) (Kleijnen, 2015), stochastic search (internal) (Andradóttir, 2015, Hu, 2015, Zabinsky, 2015) and versions of stochastic approximation (internal) where knowledge of (2) is limited to the ability to perform evaluations (Chau and…”
- Bio-inspired computation: Where we stand and what's next (2019, background): “On the other hand, stochastic optimization is a research topic that came to public attention several decades ago [86–88], with comprehensive overviews contributed to the literature ever since [89,90].”