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!?