I have a multi-objective optimization problem (NP-complete). To solve it, I've decided to use the genetic algorithm.
I have 3 "areas" of optimization, say parameters
c, so cost function for my problem is
C(F) = a + b + c -> min.
I want to somewhat modify the genetic algorithm. In the classic workflow, the initial population is created randomly. For my problem, I want to create a "smart" initial population. Since I have 3 parameters to be optimized, I wanna create 1/3 of the initial population optimized for parameter
a, 1/3 optimized for parameter
b, and 1/3 optimized for parameter
c. Then, in the while loop just "mix" these good individuals. Is this a good way to go? Is this still considered the genetic algorithm? Is this a good modification?
P.S. I'm doing research for my master's project so I need to provide some novelty in my solution. Would this modification be considered fine for that?
Thanks in advance.