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cw-envt.py: run this to see the environment in action. You can see that a random creature is generated and dropped into the arena. prepare_shapes.py:

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cw-envt.py: run this to see the environment in action. You can see that a random creature is generated and dropped into the arena. prepare_shapes.py: This is the script I used to generate the mountain. You will need to experiment with this script to generate some different shapes of mountains. Step-by-step process you should follow to complete the basic part of the course- work: Download the example code Set up a virtual environment with the appropriate packages to run the code in cw-envt.py Verify that you can run cw-envt.py and that you see the image shown above Make a copy of cw-envt.py and integrate the full genetic algorithm system you should have developed during the course so that the simulation loads up the sandbox + mountain environment and tests the creatures in there Change the fitness function so that fitness is based on ability to climb the mountain. It is up to you how to do this Carry out a series of experiments where you test different GA and genome settings, e.g. different population size and other high-level settings Produce graphs and tables describing how the different settings you tried affect the mountain climbing Advanced coursework steps: experimenting with the encod- ing scheme Once you have succeeded in integrating the new environment into your genetic algorithm, you have defined a new fitness function and you have tested it out with various settings, you are ready for the advanced part of the coursework. The advanced part involves experimenting with the genetic decoding scheme in order to explore a different space of possible creatures. Things you can experiment with are as follows: the motor controls the shape of the parts of the robot having different parts of the robot be evolvable, e.g. you do not evolve every part of the robot, just some of it There are lots of things you can try out here, for example, can you start with a fixed design robot, and just evolve the motor control parameters? Can you fix some parts of the robot so they cannot evolve, and other parts do, e.g. evolving just leg designs? Can you try some different shapes and connection settings? You should carry out further experiments wherein you try out your ideas and summarise them in graphs and tables. 4

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