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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 a, b and 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.

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Yes, this is absolutely still considered a genetic algorithm. In fact, it is quite common to experiment with various strategies of obtaining an initial population. As you say, it is reasonable to start with a completely random population unless you have a reason to try something else.

Whether this is a good way to go is anybody's guess, really. It depends heavily on your problem and problem instances. Give it a try and see how it compares to a random initial population. Further, whether this is a modification that is considered fine, trust your advisor more than random people on the internet.

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