Analyzing randomness effects on the reliability of exploratory landscape analysis
Mario Andres Munoz, Michael Kirley, Kate Smith-Miles
Natural Computing | SPRINGER | Published : 2021
The inherent difficulty of solving a continuous, static, bound-constrained and single-objective black-box optimization problem depends on the characteristics of the problem’s fitness landscape and the algorithm being used. Exploratory landscape analysis (ELA) uses numerical features generated via a sampling process of the search space to describe such characteristics. Despite their success in a number of applications, these features have limitations related with the computational costs associated with generating accurate results. Consequently, only approximations are available in practice which may be unreliable, leading to systemic errors. The overarching aim of this paper is to evaluate th..View full abstract
Awarded by Australian Research Council through the Australian Laureate Fellowship
Funding was provided by the Australian Research Council through the Australian Laureate Fellowship FL140100012, and The University of Melbourne through MIRS/MIFRS scholarships.