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Evolutionary Computation Lab
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A lab studying evolutionary computation, which reproduces the mechanisms of biological evolution on computers.
AboutAbout the lab
Evolving programs to make them more lifelike.
The Evolutionary Computation Lab studies methods for reproducing the mechanisms of biological evolution inside computers.
The lab believes this mechanism can be used both for optimization to solve engineering problems and for research that creates life within artificial systems.
It is also interested in simulations of evolving agents and of agents that behave like living things.
The lab's website is the personal research page of Associate Professor Claus Aranha, and it is written in English.
Key terms
- Evolutionary computation
- A method that reproduces biological evolution on a computer. It searches for solutions to a problem by repeating replication, mutation, and selection.
- Artificial life
- A research field that tries to understand life by creating life-like behavior in artificial systems.
- Genetic programming
- A method that treats programs themselves as genetic information and evolves them.
ResearchResearch themes
The lab reproduces the mechanisms of biological evolution inside computers, aiming both to solve engineering problems and to create life in artificial systems. Centered on evolutionary computation, it also lists artificial life and multi-agent simulation as research themes.
*Illustrative imageDecomposing evolutionary computation into components
The lab is exploring ways to decompose evolutionary computation algorithms into basic components. Its questions include how to evaluate the similarities and differences between algorithms and how to choose components that suit a given problem.
*Illustrative imageHandling multiple competing solutions
The lab is exploring how to use evolutionary computation to solve problems in which multiple solutions compete with each other.
*Illustrative imageGenetic programming that learns from experience
The lab investigates genetic programming mechanisms that improve over time and learn from past experience. One of its questions is whether a system capable of competitive programming can be built.
*Illustrative imageDiverse applications of evolutionary computation
The lab applies evolutionary computation to a variety of industrial problems. These include applications to game AI, art, and automatic generation.
MembersFaculty
- Associate ProfessorClaus Aranha
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “The Ant Colony” Sudipta1243 / CC BY-SA 4.0 (cropped)
- Decomposing evolutionary computation into components: “20251019 Replica DNA double helix model Berlin 03” Flocci Nivis / CC BY 4.0 (cropped)
- Handling multiple competing solutions: “Prey & Predator. ( )” Rison Thumboor from Thrissur, India / CC BY 2.0 (cropped)
- Genetic programming that learns from experience: “Programming code” Martin Vorel / CC BY-SA 4.0 (cropped)
- Diverse applications of evolutionary computation: “Generative art mael tsang” Gwendal Tsang / CC BY-SA 4.0 (cropped)
- Related lab (Multi-Agent Systems Lab): “Flock of munia” Abhijith Photography / CC BY-SA 4.0 (cropped)
- Related lab (Black-Box Optimization Lab): “Server Room Terminals ( )” Jon Parise / CC BY-SA 2.0 (cropped)
- Related lab (Program Logic Lab): “MIDI note player source code” Yadarwish / CC BY-SA 3.0 (cropped)
- Related lab (High Performance Computing Systems Lab): “Supercomputer Quinde I” JeffCF03 / CC BY-SA 4.0 (cropped)
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