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I have executed an optimization run, and backed up the result object using dill as proposed in the Tutorial. Let say it executed 2 iterations. As checkpoint I use the last algorithm object of its history.
When I want to resume the search for a number of generations, let say 3, I encounter that the new result object has more algorithm objects in each history then the number of iterations. While it should have 5 algorithm objects, it does have 6. So it does one additional iteration. In addition, the very last generation is lost/overwritten from the previous search.
Can you provide a short example for me to reproduce the gap in history?
Would a simple algorithm.history.append(deepcopy(algorthm)) or something similar solve your issue?
I have executed an optimization run, and backed up the result object using dill as proposed in the Tutorial. Let say it executed 2 iterations. As checkpoint I use the last algorithm object of its history.
When I want to resume the search for a number of generations, let say 3, I encounter that the new result object has more algorithm objects in each history then the number of iterations. While it should have 5 algorithm objects, it does have 6. So it does one additional iteration. In addition, the very last generation is lost/overwritten from the previous search.
Is there anything wrong with my code?
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