Jigsaw Solver Genetic Algorithm
Introduction Genetic Algorithm
The selected photo is turned into a 9-piece puzzle using the skimage view_as_blocks function. The dictionary is assigned 9 sections as the target [0,1,2,3,4,5,6,7,8]. The puzzle pieces are then mixed with the shuffle and this is done for the initial population number. In this case, the initial population is created.
Selection Function
Among the created populations, the most suitable one is selected and the next generation is created. In order to create the next generation, those with the most unsuccessful fitness values from the previous generation are removed and children formed from the best parents are added.
Fitness function
The Member travels the Population and increases its fitness value by one when the Member's Chromosome equals the Target Chromosome. This process takes place as long as the target's length. Those with great fitness value are closest to the target. When the Member's Fitness value is equal to the Target's length, the target is found.
Crossover Function
The individual (chromosome) in the received population is ranked according to the fitness value. Using selection, the two best individuals (parents) from the population are selected. If the probability value we calculated is less than 0.4, if it is between 0.4 and 0.8 from the first parent, the gene is selected for the image sequence from the second parent and the child is created.
Mutation Function
If the probability is not between these values, the mutation is applied. A picture block is added to the child randomly from the image orders. Here, duplicate values are removed in order not to add parts that already exist in the photo, and the process is added again until there are no identical orders.
