Simulating non-stationary operators in search algorithms

• Selecting most suitable operators in search algorithms.
• Simulating search operators whose behavior often changes continuously during the search.
• Comparison of operator selection policies for non-stationary search scenarios.
• Specific restless bandits whose actions values decrease according to frequent uses.

In this paper, we propose new scenarios for simulating search operators whose behaviors often change continuously during the search. In these scenarios, the performance of such operators decreases while they are applied. This is motivated by the fact that operators for optimization problems are often roughly classified into exploitation and exploration operators. Our simulation model is used to compare the performances of operator selection policies and to identify their ability to handle specific non-stationary operators. An experimental study highlights respective behaviors of operator selection policies when faced to such non-stationary search scenarios.

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