Evolutionary computation
By Kenneth A. De Jong. Annual Conference on Genetic and Evolutionary Computation, 2011
511 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/063568b1aed039b61a2bbeb2d60b04e7a8210a8e
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
- Kenneth O. Stanley
- Joel Lehman
- Pablo Hernandez-Leal
- Jeff Clune
- Shimon Whiteson
- Jim E. Smith
- Yujun Zheng
- Agoston E. Eiben
In Information Systems
- Pablo Hernandez-Leal
- Agoston E. Eiben
- Chrisantha Fernando
- Selmar K. Smit
- Kenneth O. Stanley
- Eörs Szathmáry
- P. Husbands
- Jim E. Smith
Citations in the corpus
- Multi-agent Reinforcement Learning: An Overview (2010, background): “Evolutionary computation applies principles of biological evolution to the search for solutions (agent behaviors) of the given task [2, 55 ].”
- Abandoning Objectives: Evolution Through the Search for Novelty Alone (2011, background): “Keywords Evolutionary algorithms, deception, novelty search, open-ended evolution, neuroevolution.”
- Water wave optimization: A new nature-inspired metaheuristic (2015, background): “Nature-inspired computing has been fascinating computer scientists for a long time, giving rise to popular areas such as artificial neural networks [1], cellular automata [2], molecular computing [3], and evolutionary algorithms (EAs) [4].”
- Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal (2012, methodology): “A mutated copy of the parent is placed back in the pool using inverse rank-based roulettewheel selection (De Jong, 2006).”
- Comparing parameter tuning methods for evolutionary algorithms (2009, background): “Evolutionary Algorithms (EA) form a rich class of stochastic search methods that share the basic principles of incrementally improving the quality of a set of candidate solutions by means of variation and selection [7], [5].”
- A survey and critique of multiagent deep reinforcement learning (2018, methodology): “, evolutionary methods [20,85,185,271] or sampling-based methods, have shown good results [24,156,187].”
- Harmony Search as a Metaheuristic Algorithm (2010, background): “Evolutionary algorithms are the name for a subset of evolutionary computation [ 7 ].”
- From evolutionary computation to the evolution of things (2015, background): “Evolutionary algorithms have overcome considerable initial scepticism, and over the last twenty years evolutionary computation has grown to become a major field in computational intelligence 7,8,9 .”