Racing Multi-Objective Selection Probabilities
This work addresses efficiency issues for researchers and practitioners in optimization, but it is incremental as it builds on existing methods like NSGA-II.
The paper tackled the high computational cost of approximating statistics for noisy multi-objective optimization by proposing a racing approach to dynamically estimate selection probabilities, reducing the number of required samples compared to static budget methods.
In the context of Noisy Multi-Objective Optimization, dealing with uncertainties requires the decision maker to define some preferences about how to handle them, through some statistics (e.g., mean, median) to be used to evaluate the qualities of the solutions, and define the corresponding Pareto set. Approximating these statistics requires repeated samplings of the population, drastically increasing the overall computational cost. To tackle this issue, this paper proposes to directly estimate the probability of each individual to be selected, using some Hoeffding races to dynamically assign the estimation budget during the selection step. The proposed racing approach is validated against static budget approaches with NSGA-II on noisy versions of the ZDT benchmark functions.