Simulating Non Stationary Operators in Search Algorithms

TL;DR

This paper introduces a simulation model for non-stationary search operators, enabling comparison of operator selection policies' adaptability in dynamic search scenarios, with experimental results highlighting their performance differences.

Contribution

The paper presents a novel simulation model for non-stationary search operators and evaluates how different selection policies adapt to changing operator behaviors.

Findings

Selection policies vary in adaptability to non-stationary operators.

Experimental results show distinct performance patterns among policies.

The model helps identify effective strategies for dynamic search environments.

Abstract

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.