|
gptkbp:instanceOf
|
computational paradigm
optimization technique
|
|
gptkbp:application
|
gptkb:machine_learning
robotics
optimization problems
artificial life
engineering design
|
|
gptkbp:category
|
stochastic optimization
nature-inspired algorithms
|
|
gptkbp:fieldOfStudy
|
gptkb:artificial_intelligence
computer science
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
evolutionary computation
|
|
gptkbp:includes
|
gptkb:genetic_algorithms
genetic programming
evolutionary programming
differential evolution
evolutionary strategies
|
|
gptkbp:inspiredBy
|
Darwinian evolution
|
|
gptkbp:notableConference
|
gptkb:GECCO
gptkb:PPSN
gptkb:CEC
|
|
gptkbp:notableContributor
|
gptkb:Ingo_Rechenberg
gptkb:Lawrence_J._Fogel
gptkb:John_Holland
|
|
gptkbp:notablePublication
|
gptkb:Evolutionary_Computation_(journal)
gptkb:Genetic_Programming_and_Evolvable_Machines
gptkb:IEEE_Transactions_on_Evolutionary_Computation
|
|
gptkbp:operator
|
mutant
recombination
crossover
selection
|
|
gptkbp:originatedIn
|
1960s
|
|
gptkbp:principle
|
natural evolution
|
|
gptkbp:relatedTo
|
metaheuristics
bio-inspired computation
swarm intelligence
|
|
gptkbp:usesFitnessFunction
|
examination
|
|
gptkbp:usesPopulation
|
candidate solutions
|
|
gptkbp:bfsParent
|
gptkb:Evolution
|
|
gptkbp:bfsLayer
|
4
|