Oguz K.2023-06-162023-06-1620199.78E+12https://doi.org/10.1109/ASYU48272.2019.8946413https://hdl.handle.net/20.500.14365/35052019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 -- 31 October 2019 through 2 November 2019 -- 156545Genetic 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. © 2019 IEEE.eninfo:eu-repo/semantics/closedAccessadaptive parameter modificationevolutionary algorithmsfinite state machinesgenetic algorithmsTartarus problemEarthmoving machineryEvolutionary algorithmsFinite automataGenetic algorithmsIntelligent systemsAdaptive evolutionAdaptive parametersLocal minimumsNumber of stateNumber stateParameter modificationTartarus problemParameter estimationConference Object10.1109/ASYU48272.2019.89464132-s2.0-85078323914