How we can measure the quality of an element (cell) in a candidate solution in evolutionary algorithms? For example, a gene in a chromosome? In other words, objective function calculates the quality of each candidate solutions, while I want to find a method to measure the quality of each element of the candidate solution?
Have a look at Article Evolutionary optimization of image processing for cell detec...
https://www.sciencedirect.com/topics/computer-science/evolutionary-computation
If you programmed the evolutiony algorithm by yourself, then you could to show intermediate solutions particularities in any moment of the processes evolution, so you are able to find the quality of any element of a candidate solution in evolutionary algorithms in any moment. If you use any comercial software this depends on the partiularities of the sofware used.
Salam. It can be seen with achieving sensitivity analysis. We have two type SA: local and global. Local SA evaluates the sensitivity of a variable around a specify point. But, global SA evaluates it in the search space. Then we can consider global SA in the optimization algorithms and rank the variables based on these sensitivity to objective function. regards
There are some technical programming points you should take care of them regardless of the above answers: first, you can follow what has been proposed above directly, as in such a case, maybe your solution goes infeasible when you keep a unique cell. So, your analysis will be a misleading chain of function feedbacks. Another mistake can be due to monitoring of weakly-defined fitness function, instead of monitoring the loss of a function that is sensitive to all independent genes. So, you can also change the definition of fitness function to be a another valid function, and also, it can track (sensing) each cell instead of the whole solution block. If you protect your model from these flaws or not able to redefine the objective function, you can focus on the way you programmed your code (functional programming or not). Then, you can simply eliminate the set of selected elements one by one (using a conditional selection scheme) and re-test the evaluations in the loop.
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