Fitness Landscape Analysis on Binary Dynamic Optimization Problems

Abstract

Fitness Landscape Analysis (FLA) denotes the task of analyzing black-box optimization problems and capturing their characteristic features with the goal of providing additional information, that may help in algorithm selection, parametrization or guidance. Many real-world optimization tasks require dynamic on-going optimization and a plethora of meta-heuristic algorithms has been introduced for this task. However, most analysis focuses on static problems or dynamic optimization tasks without time-linkage, where the dynamic changes of the problem are independent of the decisions taken by the optimizer, but many real-world optimization problems display very heavy dependence on previous states and decisions. In this paper, the techniques of the static FLA are combined with dynamic and domain specific measures and applied to two dynamic problems. A time-linked dynamic OneMax problem and a dynamic multi-objective knapsack problem are presented and the impact of time-linkage on their FLA features is analyzed.