| Code |
9115
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| Year |
3
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| Semester |
S2
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| ECTS Credits |
6
|
| Workload |
PL(30H)/T(30H)
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| Scientific area |
Bioengenharia
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Mode of delivery |
- Face-to-face.
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Work placements |
- N/A.
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Learning outcomes |
- Understand the key concepts and technologies used in artificial life.
- Be able to develop and implement at least one genetic algorithm.
- Be able to develop and implement at least one evolutionary algorithm.
- Be able to develop and implement at least one cellular automaton.
- Be able to develop and implement at least one neural network.
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Syllabus |
- Introduction.
- Evolution and Genetic Algorithms.
- Evolutionary Algorithms, the Microbial GA.
- Models of Growth and Deevelopment.
- Evolutionary Robotics.
- Coevolution.
- Cellular Automata and Random Boolean Networks.
- Braitenberg Vehicles, Robot Lab.
- Gaia Theory and Daisyworld Models.
- Origin of Life, Autocatalytic sets, Autopoiesis.
- Neural Networks.
- Tierra/Avida, plus Passive Dynamic Walking.
- Different Evolutionary Algorithms, GP, Classifier Systems, SAGA.
- CTRNNs as different from vanilla ANNs.
- Game Theory, Evolution of Communication.
- Information, Life and Evolution; GasNets.
- Evolution and Learning.
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Main Bibliography |
- Adamatzky, Andrew; Komosinski, Maciej (Eds.). Artificial Life Models in Software. Springer, 2005.
- Christopher G. Langton. Artificial Life: an Overview. MIT Press, Cambridge,. 1995.
- Christoph Adami. Introduction to Artificial Life. Springer; 1999.
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Language |
Portuguese. Tutorial support is available in English.
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