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dc.contributor.authorChicano, Franciscoen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorWhitley, L Darrellen_UK
dc.contributor.authorTinós, Renatoen_UK
dc.description.abstractAn optimal recombination operator for two parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this paper, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.en_UK
dc.publisherMassachusetts Institute of Technology Press (MIT Press)en_UK
dc.relationChicano F, Ochoa G, Whitley LD & Tinós R (2021) Dynastic Potential Crossover Operator. Evolutionary Computation.
dc.rightsThis is the author's final version accepted for publication in Evolutionary Computation published by MIT Press:
dc.subjectRecombination operatoren_UK
dc.subjectdynastic potentialen_UK
dc.subjectgray box optimizationen_UK
dc.titleDynastic Potential Crossover Operatoren_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleEvolutionary Computationen_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEuropean Commission (Horizon 2020)en_UK
dc.description.notesOutput Status: Forthcoming/Available Onlineen_UK
dc.contributor.affiliationUniversity of Malagaen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationColorado State Universityen_UK
dc.contributor.affiliationUniversity of Sao Pauloen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorChicano, Francisco|en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.authorWhitley, L Darrell|en_UK
local.rioxx.authorTinós, Renato|en_UK
local.rioxx.projectProject ID unknown|European Commission (Horizon 2020)|en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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