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Author: Alex Ksikes - The representation of each hypothesis is simply the coordinates (in binary) of the 8 queens on the board. Though I converted them to integers to easily compute the fitness function. for example: h1 010 001 000 100 .... first queen at (2,1), second queen at (0,4) .... - I used a rank selection to probabilistically select the best hypotheses, Pr(h)=(p-rank(h))/(Sumi p-rank(hi)). - I used a uniform crossover. - The mutation operator simply flips one bit of an hypothesis. - Inconsistent hypotheses have more than one queen in a given coordinate, I assigned to them a very low fitness (for my case a very high fitness). - The fitness function simply counts the number of attacks from one queen to the others. - The problem is solved when there is an hypothesis of fitness = 0. It seems that the best set of parameters are p=33, r=0.6 and m=0.1