How to identify the individuals not satisfying non-linear inequality constraints?
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I am using Genetic Algorithm to solve a problem which involves minimising a fitness function, subject to some non-linear inequality constraints and decision variables subject to integer constraint. I am setting Penalty as a non-linear constraint algorithm which adds a penalty to the fitness value of infeasible individual (not satisfying the constraint) depending on the constraint violation and worst feasible fitness value in that population.
I am also saving GA population history and scores during all generations. Now, I want to visualise that during each generation which individuals didn't satisfy the constraint and caused a penalty in fitness value?
I want to show whole optimisation process like individuals during 1st generation (feasible and infeasible), with corresponding fitness value and evolving to the optimum solution till last generation
5 Kommentare
Gifari Zulkarnaen
am 20 Feb. 2020
If you know what are the constraints, make a constraint function, then input the individuals to get the constraint output (for knowing the infeasibility). Do you know the constraints?
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