When to apply soft constraints evaluation functions in genetic algorithms? As stated in the question, say i created a random population of 100 timetables. 1 % of these timetables are valid. Then, if I apply the soft constraints evaluation functions on the 1% valid timetables, the population will have few outstanding finesses like below:

population = 5;

finesses = { 0.121,0.115,0.117,0.855,0.145} , the one in bold is an outstanding fitness and has the soft constraints evaluation functions applied on it. i have two hard constraints and three soft constraints and i use the weighted average to calculate overall fitness like this: (0.05)*h1+(0.05)*h2+(0.3)*s1+(0.3)*s2+(0.3)*s3

the question is:
Is this approach correct ?

someone suggested that i should treat soft and hard constraints the same, but if i do how can one soft constraints evaluate an invalid timetable!?
Experiment.  It depends on the topology of your problem domain. If you knew how to optimize more efficiently you wouldn't be throwing a GA at it.