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##Evolutionary Creativity

When Darwin published his theories about evolution, he presented two key mechanisms responsible for the evolution (natural selection and sexual selection). While the natural selection has widely accepted by the scientific community, the sexual selection has highly criticised and so it was forgotten over time. It was only nearly a century later, that the sexual selection began to be acknowledged, mainly due the researches of Fisher and Zahavi. In the last decades, other matter that has intrigued the scientific community is the reasons that lead an individual to join a herd and how to describe the movements observed in these herds. Some authors, among which Reynolds, suggest that the flocking behaviour emerges through simple motion rules.

Afterwards, hybrid algorithms combining the ideas from evolution and motion of flocks were presented. In such algorithms, the motion rules evolve over the time. Inspired in these algorithms, in this dissertation is proposed a new algorithm, which inserts in these hybrid models the ideas from sexual selection, in particular mate choice. In the end, the emergence of a flocking behaviour is expected and the effects of using mate choice instead of the traditional approaches are analysed.

Inspired in these algorithms, in this dissertation is proposed a new algorithm, which inserts in these hybrid models the ideas from sexual selection, in particular mate choice. The proposed algorithm combines the ideas from algorithms of flocking behaviour with the ideas of a variant of evolutionary algorithms that uses mate choice instead of the traditional methods. In the end, the emergence of a flocking behaviour is expected and the effects of using mate choice instead of the traditional approaches are analysed.


The objective of this project is to combine the ideas from algorithms of flocking behavior with the ideas of a variant of evolutionary algorithms, that uses mate choice instead of the traditional methods. The proposed algorithm is available can be found in the file named "BOID_v2.0" which is within the folder "demos".

##Installation

This program runs in BREVE simulation environment and therefore it's necessary to have BREVE installed to run this project.

  1. If you are a Windows user download this repository and open the following executable "Evolutionary-Creativity/breveIDE_windows_2.7.2_2/breveIDE_2.7.2/breveIDE.exe".
  2. If you use another OS or if you are not able to open the executable, then you need to download a version of BREVE. BREVE is available for free in BREVE, also some versions are available within this repository in the folder named "Software". After you finished downloading the framework copy the following folder Thesis to inside your Demos folder, then launch the framework.
  3. At the top bar you need to navigate between "Demos > Thesis > BOID_v2.0.py" to open the developed simulation.
  4. Now you can change the properties of the simulation through the alteration of their variables or you can run the simulation with the default properties.
  5. Finally to run the simulation you must press the "play button" that lies at the bottom bar to run the simulation.

##Demo

Older Versions:

Evolutionary Creativity_v1.0

Current Version:

Evolutionary Creativity_v2.0

##Bug Reports & Feature Requests

You can help by reporting bugs, suggesting features, reviewing feature specifications or just by sharing your opinion.

Use GitHub Issues for all of that.

Contributing

  1. Fork it!
  2. Create your feature branch: git checkout -b my-new-feature
  3. Commit your changes: git commit -am 'Add some feature'
  4. Push to the branch: git push origin my-new-feature
  5. Submit a pull request :D

License

This project is licensed under the terms of the MIT license. See LICENSE for more details.

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