Introduction to Evolutionary Computing 2nd Edition

By Agoston E. Eiben, J. E. Smith. 2020

223 citations recorded by the source corpus.

Subfields: Natural Language Processing, 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 Natural Language Processing

1. Ekaterina Shutova
2. Helen Yannakoudakis
3. Nithin Holla
4. Santhosh Rajamanickam

In Optimization & Training

1. Nithin Holla
2. Santhosh Rajamanickam
3. Seppe vanden Broucke
4. Eugen Stripling
5. Helen Yannakoudakis
6. Ekaterina Shutova
7. Bart Baesens
8. Sebastiaan Höppner

Citations in the corpus

• A Multimodal Framework for the Detection of Hateful Memes (2020, methodology): “We experimented with different number of cross-validation folds, but our final model is trained under a 15-fold cross-validation setting and we optimize the ensemble weights using an evolutionary algorithm with tournament selection, Gaussian noise mutation and uniform crossover [8, 24] strategies.”
• Profit Driven Decision Trees for Churn Prediction (2017, methodology): “Evolutionary algorithms An evolutionary algorithm (EA) is a metaheuristic algorithm inspired by the biological process of evolution that allows solving complex optimization problems (Eiben and Smith, 2015; Yu and Gen, 2010).”