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PBI-BFS-MaOA: A Many-Objective Evolutionary Algorithm with PBI-Based Boundary-Front Selection

Reference-guided many-objective evolutionary al-gorithms often lose selection pressure when Pareto dominance becomes scarce and the final accepted front must be truncated. We propose PBI-BFS-MaOA, a many-objective evolutionary algorithm that preserves Pareto ranking for feasible solutions and modifi...

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Pubblicato in:Latin-American Journal of Computing (LAJC)
Autori principali: Thiago Santos, Sebastiao Xavier
Natura: Artigo
Lingua:Inglês
Pubblicazione: Escuela Politécnica Nacional 2026
Soggetti:
Computación
Many
penalty
reference directions
objective optimization
environmental selection
Accesso online:https://www.redalyc.org/articulo.oa?id=823985609004
https://www.redalyc.org/journal/8239/823985609004/
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https://www.redalyc.org/journal/8239/823985609004/823985609004.epub
https://www.redalyc.org/journal/8239/823985609004/movil
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