Simulating Non Stationary Operators in Search Algorithms
This work addresses the challenge of adapting operator selection in optimization algorithms for scenarios where operator performance is non-stationary, but it appears incremental as it focuses on simulation and comparison rather than introducing a new method.
The paper tackles the problem of simulating search operators with continuously changing behavior, which degrade in performance over time, by proposing a model to compare operator selection policies and their adaptation abilities. The experimental study yields results on policy behaviors in non-stationary search scenarios.
In this paper, we propose a model for simulating search operators whose behaviour often changes continuously during the search. In these scenarios, the performance of the operators decreases when they are applied. This is motivated by the fact that operators for optimization problems are often roughly classified into exploitation operators and exploration operators. Our simulation model is used to compare the different performances of operator selection policies and clearly identify their ability to adapt to such specific operators behaviours. The experimental study provides interesting results on the respective behaviours of operator selection policies when faced to such non stationary search scenarios.