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Open AccessFeature PaperArticle

Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions

1
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
2
Departamento de Computación, CINVESTAV-IPN, Mexico City 07360, Mexico
3
School of Engineering, University of California Merced, Merced, CA 95343, USA
*
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2020, 25(1), 3; https://doi.org/10.3390/mca25010003
Received: 16 October 2019 / Revised: 18 December 2019 / Accepted: 25 December 2019 / Published: 8 January 2020
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
In this paper, we present a novel evolutionary algorithm for the computation of approximate solutions for multi-objective optimization problems. These solutions are of particular interest to the decision-maker as backup solutions since they can provide solutions with similar quality but in different regions of the decision space. The novel algorithm uses a subpopulation approach to put pressure towards the Pareto front while exploring promissory areas for approximate solutions. Furthermore, the algorithm uses an external archiver to maintain a suitable representation in both decision and objective space. The novel algorithm is capable of computing an approximation of the set of interest with good quality in terms of the averaged Hausdorff distance. We underline the statements on some academic problems from literature and an application in non-uniform beams. View Full-Text
Keywords: evolutionary multi-objective optimization; nearly optimal solutions; non-uniform beams evolutionary multi-objective optimization; nearly optimal solutions; non-uniform beams
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Hernández Castellanos, C.I.; Schütze, O.; Sun, J.-Q.; Ober-Blöbaum, S. Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions. Math. Comput. Appl. 2020, 25, 3.

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