How can information theory illuminate the rugged terrain of fitness landscapes? This paper introduces a novel information analysis approach to understanding the structure of fitness landscapes, which are often used to model optimization problems. Instead of focusing solely on correlation characteristics, this method considers a fitness landscape as an ensemble of objects related to the fitness of neighboring points. The study defines and explores three key information characteristics: information content, partial information content, and information stability. These characteristics are then applied to a range of landscapes with known correlation features, allowing for a comparative analysis of the information analysis approach. The results demonstrate that the proposed information analysis provides valuable insights into the structure of fitness landscapes, offering a complementary tool for investigating landscape ruggedness and guiding the development of optimization algorithms.
Published in _Evolutionary Computation_, this paper aligns with the journal's focus on computational methods inspired by natural evolution. The introduction of information analysis to study fitness landscapes offers a novel perspective for evolutionary algorithms and optimization, contributing to the journal's core themes.