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Differential evolution

Differential evolution (DE) is an evolutionary algorithm to optimize a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the optimized problem and can search very large spaces of candidate solutions. However…

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Differential evolution

Nodes23
Edges22
Triples19
Avg. degree1.91
Density0.086957
Components1

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Differential evolution

Top relations

related to Constraint handling · 10
Differential evolution → Conversely, CV, Despite, Differential, Here, If, L1, L2, One, This
related to history · 5
Differential evolution → Books, DE, Price, Storn, Surveys

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Important terminology

displaystyle candidate solution de population optimization mathbf problem cr agents agent random pick algorithm position number called index differential new

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
DE do not guarantee an optimal solution is ever found.DE is used for multidimensional real-valued functions but does not use the gradient of the problem being optimizedinstance ofmetaheuristics0.80text
which means DE does not require the optimization problem to be differentiableinstance ofmetaheuristics0.80text
as is required by classic optimization methods such as gradient descentinstance ofmetaheuristics0.80text
quasi-newton methodsinstance ofmetaheuristics0.80text
Differential evolutionrelated to Constraint handlingDifferential0.60section
Differential evolutionrelated to Constraint handlingCV0.60section
Differential evolutionrelated to Constraint handlingHere0.60section
Differential evolutionrelated to Constraint handlingL10.60section
Differential evolutionrelated to Constraint handlingL20.60section
Differential evolutionrelated to Constraint handlingThis0.60section
Differential evolutionrelated to Constraint handlingOne0.60section
Differential evolutionrelated to Constraint handlingIf0.60section

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