Penerapan algoritma genetika hybrid dalam permainan puzzle tetravex = Implementation of hybrid genetic algorithm in tetravex puzzle game

Christysen, Fery (2012) Penerapan algoritma genetika hybrid dalam permainan puzzle tetravex = Implementation of hybrid genetic algorithm in tetravex puzzle game. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Tetravex is an edge matching puzzle which requires player to move some pieces of puzzle to the designated place. The piece of puzzle is a square tile with 4 numbers on top, right, left and bottom of the tile. The tile must be arranged in such way so every number on adjacent faces of tile matches. There is no paper has yet to discussed the solver of the game. According to that, this research is conducted to solve the puzzle and develop the software. The proposed solution is by using hybrid genetic algorithm. One gene in the algorithm represent a tile in the puzzle, and the sequences of genes which form the chromosome represent the proposed solution of the puzzle. First, a set number of chromosomes are randomly generated, then a fitness function is applied to calculate the fitness value of each chromosomes. Then selection phase is done with Roulette Wheel while Sequential Constructive Crossover is used for the crossover phase. After that, mutation phase is taken place. The ordinary genetic algorithm use mutation which change a random selected gene, but in this research a Breadth First Mutation is used instead to accelerate the searching process. These 3 phases are repeated until a certain maximum generation(searching considered fail) or at least one chromosome fulfills a required fitness value(searching considered success). The result of the implementation is that the hybrid genetic algorithm have higher chance of success and requires less generation to reach solution than the ordinary genetic algorithm. Also, the result concluded that the parameter such as puzzle size, population, crossover rate and mutation rate affect the chance of success and generation to reach solution. The best parameter for the hybrid genetic algorithm is 100 chromosomes for population, 1.0 for crossover rate and 1.0 for mutation rate.

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