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We can reshape the board by stacking the rows to create a chromosome of 100 genes.
Since each gene in the board solution can either be a one or a zero, our toy problem could have been arranged in 2¹⁰⁰ or 1.27e 30 possible ways!
While accurate in content, this is not necessarily a sequence that would build a student's confidence in their problem-solving abilities. There is also a good index, although I was troubled that some reference material, such as the Chi square statistical table (Table 1.1), pedigree symbols (Figure 2.1), and the genetic code (Table 9.1), are placed in the text of a chapter but have no index entries of their own.
That makes them inconvenient to find when doing later problem sets or class homework.
We can express the board as a binary representation simply by denoting the squares occupied by our ships as ones and the unoccupied squares as zeros.
We can randomly position each of our ships by following a few simple steps: This results in a two-dimensional binary matrix, which must be transformed into a one-dimensional array that conforms to the genetic form.To intuitively understand the practical implementation and fundamental requirements for employing genetic algorithms, we can set up a toy problem and solve the board of the classic guessing game, board is composed of a 10 x 10 grid, upon which we can randomly place five ships of varying length.Our fleet includes a carrier (5), a battleship (4), a cruiser (3), a submarine (3) and a destroyer (2).No text is going to satisfy everyone in all details, but I found this one to be quite well done and of value to students who will use it as intended.In other words, students need to try working the problems on their own, and not just go straight to the answers as less disciplined students are wont to do.As an assist to self-study, students gain practice in using their knowledge, much as they will ultimately do on examinations and in professional applications of this information.Even if they are sometimes left with questions about how to handle certain material, they will at least have a good idea of where their confusion lies.But sometimes, such as in the pedigree problems, additional explanation would be helpful.For example, in one pedigree problem the first question is ‘What is the probable mode of inheritance? The second question, ‘Are you positive about this conclusion? The third question asks why they are not certain they gave the right answer the first time, and the answer in the book is ‘Could also be sex-limited’.The genetic algorithm can now learn from this fitness metric to suggest an even higher performing chromosome.can be as sophisticated as desired to provide more informative feedback.