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I would like to empirically investigate reasonable box constraint handling in the continuous and discrete case in the CMA-ES.
This issue is related to #136.
The text was updated successfully, but these errors were encountered:
I found several related papers, which draws the following opinions.
From [1], the current implementation of cmaes using resampling is somewhat reasonable.
However, from [2], the performance of resampling becomes much worse when the optimum lies on near the boundary, compared to penalty-based methods. (But it is difficult to use penalty method for us, as ask-and-tell interface urges us to use the same points between the evaluation and the update.)
In conclusion, using resampling is not bad choice for now, but it may be a good idea to allow the use of the penalty method when the ask-and-tell interface is not used.
[1] Handling bound constraints in CMA-ES: An experimental study, Rafał Biedrzycki, Swarm and Evolutionary Computation, 2020.
[2] Adaptive Ranking-Based Constraint Handling for Explicitly Constrained Black-Box Optimization, Naoki Sakamoto, Youhei Akimoto, Evolutionary Computation, 2022.
Motivation
I would like to empirically investigate reasonable box constraint handling in the continuous and discrete case in the CMA-ES.
This issue is related to #136.
The text was updated successfully, but these errors were encountered: