Q:

GA Chromosome Representation with bits of different importance

In a genetic algorithm, is it ok to encode the chromosome in a way such that some bits have more importance than other bits in the same chromosome? For example, the (index%2==0)/(2,4,6,..) bit is more important than (index%2!=0)/(1,3,5,..) bits. For example, if the bit 2 has value in range [1,5], we consider the value of bit 3, and if the bit 2 has value 0, the value of bit 3 makes no effect.
For example, if the problem is that we have multiple courses to be offered by a school and we want to know which course should be offered in the next semester and which should not, and if a course should be offered who should teach that course and when he/she should teach it.  So one way to represent the problem is to use a vector of length 2n, where n is the number of courses. Each course is represented by a 2-tuple (who,when), where when is when the course should be taught and who is who should teach it.  The tuple in the i-th position holds assignment for the i-th course. Now the possible values for who are the ids of the teachers [1-10], and the possible values for when are all possible times plus 0, where 0 means at no time which means the course should not be offered. 
Now is it ok to have two different tuples with the same fitness? For instance, (3,0) and (2,0) are different values for the i-th course but they mean the same thing, this course should not be offered since we don't care about who if when=0. Or should I add 0 to who so that 0 means taught by no one and a tuple means that the corresponding course should not be offered if and only if its value is (0,0).  But how about (0,v) and (v,0), where v>0? should I consider these to mean that the course should not be offered?  I need help with this please.

A:

I'm not sure I fully understand your question but I'll try to answer as best I can. 
When using genetic algorithms to solve problems you can have a lot of flexibility in how it's encoded. Broadly, there are two places where certain bits can have more prominence: In the fitness function or in the implementation of the algorithms (namely selection, crossover and mutation). If you want to change the prominence of certain bits in the fitness function I'd go ahead. This would encourage the behaviour you want and generally lead towards a solution where certain bits are more prominent. 
I've worked with a lot of genetic algorithms where the fitness function gives some bits (or groupings of bits) more weight than others. It's fairly standard.
I'd be a lot more careful when making certain bits more prominent than others in the genetic algorithm implementation. I've worked with algorithms that only allow certain bits to mutate, or that can only crossover at certain points. Sometimes they can work well (sometimes they're necessary given the problem) but for the most part they're a lot harder to get right, and more prone to problems like premature convergence.
EDIT:
In answer to the second part of your question, and your comments:
The best way to deal with situations where a course should not be offered is probably in the fitness function. Simply give a low score (or no score) to these. The same applies to course duplicates in a chromosome. In theory, this should discourage them from becoming a prevalent part of your population. Alternatively, you could apply a form of "culling" every generation, which completely removes chromosome which are not viable from the population. You can probably mix the two by completely excluding chromosomes with no fitness score from selection. 
From what you've said about the problem it sounds like having non-viable chromosomes is probably going to be common. This doesn't have to be a problem. If your fitness function is encoded well, and you use the correct selection and crossover methods it shouldn't be an issue. As long as the more viable solutions are fitter you should be able to evolve a good solution.
In some cases it's a good idea to stop crossover at certain points in the chromosomes. It sounds like this might be the case, but again, without knowing more about your implementation it's hard to say.
I can't really give a more detailed answer without knowing more about how you plan to implement the algorithm. I'm not really familiar with the problem either. It's not something I've ever done. If you add a bit more detail on how you plan to encode the problem and fitness function I may be able to give more specific advise.

