Reference Set Generator: A Method for Pareto Front Approximation and Reference Set Generation
Mathematics, vol. 13, pp. 1626
Abstract
In this paper, we address the problem of obtaining bias-free and complete finite size approximations of the solution sets (Pareto fronts) of multi-objective optimization problems (MOPs). Such approximations are, in particular, required for the fair usage of distance-based performance indicators, which are frequently used in evolutionary multi-objective optimization (EMO). If the Pareto front approximations are biased or incomplete, the use of these performance indicators can lead to misleading or false information. To address this issue, we propose the Reference Set Generator (RSG), which can, in principle, be applied to Pareto fronts of any shape and dimension. We finally demonstrate the strength of the novel approach on several benchmark problems.
Authors 3
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Angel E. Rodríguez-Fernandez corresponding
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional · Instituto Politécnico Nacional
Affiliation as printed
Departmento de Computación, Centro de Investigación y de Estudios Avanzados del IPN, Mexico City 07360, Mexico
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Hao Wang corresponding Aachen Leiden Institute of Advanced Computer Science and Applied Quantum Algorithms
Affiliation as printed
Leiden Institute of Advanced Computer Science and Applied Quantum Algorithms, Leiden University, 2311 EZ Leiden, The Netherlands
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Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional · Instituto Politécnico Nacional
Affiliation as printed
Departmento de Computación, Centro de Investigación y de Estudios Avanzados del IPN, Mexico City 07360, Mexico
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