Handling the balance of operators in evolutionary algorithms through a weighted Hill Climbing approach

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dc.contributor.authorRodríguez Esparza, Erick
dc.contributor.authorMorales Castañeda, Bernardo
dc.contributor.authorCasas Ordaz, Ángel
dc.contributor.authorOliva, Diego
dc.contributor.authorNavarro, Mario A.
dc.contributor.authorValdivia, Arturo
dc.contributor.authorHoussein, Essam H.
dc.date.accessioned2025-11-03T09:42:14Z
dc.date.available2025-11-03T09:42:14Z
dc.date.issued2024-06-21
dc.date.updated2025-11-03T09:42:14Z
dc.description.abstractEvolutionary Algorithms (EAs) are a well-known domain within Artificial Intelligence. EAs have demonstrated their ability to tackle intricate optimization problems using evolutionary theory principles. However, balancing the dual exploration and exploitation processes remains a crucial concern. This paper introduces the Balanced Hill Climbing Weight Algorithm with Diversity (BHWEAD), an innovative approach that combines elements from classic Genetic Algorithm and Differential Evolution. BHWEAD uniquely employs the Hill Climbing local search to guide the influence of its operators, ensuring an optimal interplay between exploration and exploitation. Additionally, it incorporates a diversity control mechanism, resetting specific solutions to prevent premature convergence to suboptimal solutions. The main contribution of the BHWEAD is the mechanism that permits the balance of the exploration and exploitation stages; also, the incorporation of Hill Climbing permits a proper balance of the influence of the operators. Notice that the proposal can escape from suboptimal solutions using a diversity-based strategy. Tested against the CEC2017 benchmark functions in both 50 and 100 dimensions, BHWEAD outperformed 12 notable EAs, underscoring its potential for high-dimensional optimization problems. Besides, the proposed BHWEAD has also been tested over seven engineering problems, and the comparisons include some memetic algorithms., The paper provides additional insights into the algorithm's design, conducts a comparative analysis, and identifies potential areas for improvement.en
dc.identifier.citationRodríguez-Esparza, E., Morales-Castañeda, B., Casas-Ordaz, A., Oliva, D., Navarro, M. A., Valdivia, A., & Houssein, E. H. (2024). Handling the balance of operators in evolutionary algorithms through a weighted Hill Climbing approach. Knowledge-Based Systems, 294. https://doi.org/10.1016/J.KNOSYS.2024.111784
dc.identifier.doi10.1016/J.KNOSYS.2024.111784
dc.identifier.issn0950-7051
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4212
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2024 Elsevier B.V.
dc.subject.otherBalance operators
dc.subject.otherDiversity control
dc.subject.otherExploitation
dc.subject.otherExploration
dc.subject.otherHill Climbing
dc.titleHandling the balance of operators in evolutionary algorithms through a weighted Hill Climbing approachen
dc.typejournal article
dcterms.accessRightsmetadata only access
oaire.citation.titleKnowledge-Based Systems
oaire.citation.volume294
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