Brasil | Actas de congresos
dc.contributorUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-05-01T13:41:31Z
dc.date.accessioned2022-12-20T03:48:46Z
dc.date.available2022-05-01T13:41:31Z
dc.date.available2022-12-20T03:48:46Z
dc.date.created2022-05-01T13:41:31Z
dc.date.issued2021-01-01
dc.identifierLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12702 LNCS, p. 109-118.
dc.identifier1611-3349
dc.identifier0302-9743
dc.identifierhttp://hdl.handle.net/11449/234120
dc.identifier10.1007/978-3-030-93420-0_11
dc.identifier2-s2.0-85124285298
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5414221
dc.description.abstractThe continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of them remains a challenge due to the overwhelming amount of candidate solutions to be evaluated, even by using sophisticated algorithms. In such a context, a set of nature-inspired stochastic methods, called meta-heuristic optimization, can provide robust approximate solutions to different kinds of problems with a small computational burden, such as derivative-free real function optimization. Nevertheless, these methods may converge to inadequate solutions if the function landscape is too harsh, e.g., enclosing too many local optima. Previous works addressed this issue by employing a hypercomplex representation of the search space, like quaternions, where the landscape becomes smoother and supposedly easier to optimize. Under this approach, meta-heuristic computations happen in the hypercomplex space, whereas variables are mapped back to the real domain before function evaluation. Despite this latter operation being performed by the Euclidean norm, we have found that after the optimization procedure has finished, it is usually possible to obtain even better solutions by employing the Minkowski p-norm instead and fine-tuning p through an auxiliary sub-problem with neglecting additional cost and no hyperparameters. Such behavior was observed in eight well-established benchmarking functions, thus fostering a new research direction for hypercomplex meta-heuristic optimization.
dc.languageeng
dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceScopus
dc.subjectBenchmarking functions
dc.subjectEuclidean norm
dc.subjectHypercomplex space
dc.subjectMeta-heuristic optimization
dc.subjectReal-valued projection
dc.titleEnhancing Hyper-to-Real Space Projections Through Euclidean Norm Meta-heuristic Optimization
dc.typeActas de congresos


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