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Genetic
representations in the optimization of three-dimensional agents |
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Abstract |
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Optimization of 3D agents is a challenging task due to a number of
reasons. The sources of difficulty include infinite, discrete-continuous search
space, solutions containing variable amounts of information, strong
dependencies between parts of a solution, multiple local optima, numerous
constraints, and the coevolution of bodies (structures, designs) and their
controllers (e.g., neural networks). This talk focuses on the key aspect that
influences the efficiency of optimization, namely the genetic representation
and its accompanying reconfiguration operators. I will demonstrate examples
of 3D agents in various real-life applications, describe a common model used
to compare various genetic encodings, and show sample results of evolutionary
experiments. Finally, I will show a high-level interface to the Framsticks environment that facilitates experimentation,
testing, and comparisons of different optimization algorithms in programming
languages like Python. Time&Place 2022.12.01
16: 05, Zoom Zoom:
https://pwr-edu.zoom.us/j/98375229553?pwd=dUV6TWdJNDN3SytwdzVLTXdzUk5yQT09 |
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Tutorial
length |
1 hour |
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Tutorial
level |
introductory |
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