Code |
9115
|
Year |
3
|
Semester |
S2
|
ECTS Credits |
6
|
Workload |
PL(30H)/T(30H)
|
Scientific area |
Bioengenharia
|
Mode of delivery |
- Face-to-face.
|
Work placements |
- N/A.
|
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.
|
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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