Journal article

Analyzing randomness effects on the reliability of exploratory landscape analysis

Mario Andres Munoz, Michael Kirley, Kate Smith-Miles



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..

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Awarded by Australian Research Council through the Australian Laureate Fellowship

Funding Acknowledgements

Funding was provided by the Australian Research Council through the Australian Laureate Fellowship FL140100012, and The University of Melbourne through MIRS/MIFRS scholarships.