Set-based Multiobjective Fitness Landscapes: A Preliminary Study
This work addresses a gap in understanding multiobjective optimization problems for researchers in evolutionary computation, but it is incremental as it builds on prior proposals.
The study tackled the lack of knowledge on multiobjective optimization landscapes by defining set-based fitness landscapes and conducting an experimental analysis on multiobjective NK-landscapes with objective correlation, resulting in preliminary insights to enhance search algorithm design.
Fitness landscape analysis aims to understand the geometry of a given optimization problem in order to design more efficient search algorithms. However, there is a very little knowledge on the landscape of multiobjective problems. In this work, following a recent proposal by Zitzler et al. (2010), we consider multiobjective optimization as a set problem. Then, we give a general definition of set-based multiobjective fitness landscapes. An experimental set-based fitness landscape analysis is conducted on the multiobjective NK-landscapes with objective correlation. The aim is to adapt and to enhance the comprehensive design of set-based multiobjective search approaches, motivated by an a priori analysis of the corresponding set problem properties.