Machine learning-enabled globally guaranteed evolutionary computation

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dc.contributor.authorLi, Bin
dc.contributor.authorWei, Ziping
dc.contributor.authorWu, Jingjing
dc.contributor.authorYu, Shuai
dc.contributor.authorZhang, Tian
dc.contributor.authorZhu, Chunli
dc.contributor.authorZheng, Dezhi
dc.contributor.authorGuo, Weisi
dc.contributor.authorZhao, Chenglin
dc.contributor.authorZhang, Jun
dc.date.accessioned2023-04-26T10:02:48Z
dc.date.available2023-04-26T10:02:48Z
dc.date.issued2023-04-10
dc.description.abstractEvolutionary computation, for example, particle swarm optimization, has impressive achievements in solving complex problems in science and industry; however, an important open problem in evolutionary computation is that there is no theoretical guarantee of reaching the global optimum and general reliability; this is due to the lack of a unified representation of diverse problem structures and a generic mechanism by which to avoid local optima. This unresolved challenge impairs trust in the applicability of evolutionary computation to a variety of problems. Here we report an evolutionary computation framework aided by machine learning, named EVOLER, which enables the theoretically guaranteed global optimization of a range of complex non-convex problems. This is achieved by: (1) learning a low-rank representation of a problem with limited samples, which helps to identify an attention subspace; and (2) exploring this small attention subspace via the evolutionary computation method, which helps to reliably avoid local optima. As validated on 20 challenging benchmarks, this method finds the global optimum with a probability approaching 1. We use EVOLER to tackle two important problems: power grid dispatch and the inverse design of nanophotonics devices. The method consistently reached optimal results that were challenging to achieve with previous state-of-the-art methods. EVOLER takes a leap forwards in globally guaranteed evolutionary computation, overcoming the uncertainty of data-driven black-box methods, and offering broad prospects for tackling complex real-world problems.en_UK
dc.identifier.citationLi B, Wei Z, Wu J, et al., (2023) Machine learning-enabled globally guaranteed evolutionary computation. Nature Machine Intelligence, Volume 5, April 2023, pp. 457-467en_UK
dc.identifier.issn2522-5839
dc.identifier.urihttps://doi.org/10.1038/s42256-023-00642-4
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19547
dc.language.isoenen_UK
dc.publisherNature Publishing Groupen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleMachine learning-enabled globally guaranteed evolutionary computationen_UK
dc.typeArticleen_UK

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