Multimodal Optimization by Means of Evolutionary Algorithms

By Mike Preuss. 2015

121 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

1. Frank Hutter
2. Holger H. Hoos
3. Ilya Loshchilov
4. F. Neumann
5. Quoc V. Le
6. Pascal Kerschke
7. Heike Trautmann
8. Aaron Klein

In Computer Networks and Communications

1. Frank Hutter
2. Holger H. Hoos
3. Ilya Loshchilov
4. F. Neumann
5. Heike Trautmann
6. Pascal Kerschke
7. Kevin Leyton-Brown
8. Katharina Eggensperger

In Optimization & Training

1. Frank Hutter
2. Ilya Loshchilov
3. Holger H. Hoos
4. Quoc V. Le
5. Ronghang Hu
6. Dan Hendrycks
7. F. Neumann
8. Heike Trautmann

Citations in the corpus

• SGDR: Stochastic Gradient Descent with Warm Restarts (2016, methodology): “Gradient-free optimization approaches based on niching methods (Preuss, 2015) usually can deal with this task by covering the search space with dynamically allocated niches of local optimizers.”
• Automated Algorithm Selection: Survey and Perspectives (2018, background): “During that time, researchers also focused on problem multimodality (Preuss, 2015), that is, the analysis of the problems’ landscapes w.”