Adaptive Evolution of Finite State Machines for the Tartarus Problem

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@InProceedings{Oguz:2019:ASYU,

• author = "Kaya Oguz",
• booktitle = "2019 Innovations in Intelligent Systems and Applications Conference (ASYU)",
• title = "Adaptive Evolution of Finite State Machines for the {Tartarus} Problem",
• year = "2019",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ASYU48272.2019.8946413",
• month = oct,
• notes = "Also known as \cite{8946413}",

}

Genetic Programming entries for Kaya Oguz

Citations