Adaptive Evolution of Finite State Machines for the Tartarus Problem
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- @InProceedings{Oguz:2019:ASYU,
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author = "Kaya Oguz",
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booktitle = "2019 Innovations in Intelligent Systems and
Applications Conference (ASYU)",
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title = "Adaptive Evolution of Finite State Machines for the
{Tartarus} Problem",
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year = "2019",
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abstract = "Genetic algorithms can be used to evolve finite state
machines for problems that require a large number of
states and transitions. Tartarus problem is such a
problem in which the purpose is to push the boxes
towards the walls of a six by six grid using a
bulldozer that can only sense its 8-neighbourhood. The
bulldozer can rotate left, right, or move forward, each
taking a single move out of its initial 80 moves. The
result is scored by the number of boxes that are
against a wall when the bulldozer is out of moves.
Several approaches have been proposed, with genetic
algorithms being the most common. We are proposing a
representation of the problem using varying number of
states and adaptive modification of the mutation
parameter to decrease the probability of the population
getting stuck at a local minima. Our results show
improvement over the application of the genetic
algorithm without parameter modification and dependency
on the number states and the size of the population.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ASYU48272.2019.8946413",
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month = oct,
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notes = "Also known as \cite{8946413}",
- }
Genetic Programming entries for
Kaya Oguz
Citations