Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

By Katharina Holzinger, Vasile Palade, Raul Rabadan, Andreas Holzinger. 2014

30 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering

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. Constantinos S. Pattichis
2. Douglas B. Kell
3. Chris Biemann
4. Andreas Holzinger
5. Jens Christian Bjerring
6. Jacob Busch
7. Heimo Müller
8. Kurt Zatloukal

In Biomedical Engineering

1. Constantinos S. Pattichis
2. Andreas Holzinger

Citations in the corpus

• What do we need to build explainable AI systems for the medical domain? (2017, background): “Very useful for building explainable systems in the medical domain is generally ge-netic programming (Koza, 1994; Pena-Reyes and Sipper, 1999; Tsakonas et al., 2004), and specifically evolutionary algorithms (Wang and Palade, 2011; Holzinger et al., 2014, 2016).”
• Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach (2016, unknown): “Further challenges are in the transfer of our approach to other nature inspired algorithms which have a lot of potential for the support of solving hard problems [43].”