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Abstract:
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Can symmetry be utilized as a design principle to constrain
evolutionary search , making it more effective ? This dissertation aims
to show that this is indeed the case , in two ways . First , an approach
called ENSO is developed to evolve modular neural network controllers
for simulated multilegged robots . Inspired by how symmetric organisms
have evolved in nature , ENSO utilizes group theory to break symmetry
systematically , constraining evolution to explore promising regions of
the search space . As a result , it evolves effective controllers even
when the appropriate symmetry constraints are difficult to design by
hand . The controllers perform equally well when transferred from
simulation to a physical robot . Second , the same principle is used to
evolve minimal -size sorting networks . In this different domain , a
different instantiation of the same principle is effective : building
the desired symmetry step -by -step . This approach is more scalable
than previous methods and finds smaller networks , thereby
demonstrating that the principle is general . Thus , evolutionary
search that utilizes symmetry constraints is shown to be effective in
a range of challenging applications . |