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gptkbp:instanceOf
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gptkb:algorithm
gptkb:mathematical_optimization
gptkb:search_heuristic
|
|
gptkbp:application
|
gptkb:machine_learning
robotics
optimization
game playing
scheduling
engineering design
|
|
gptkbp:category
|
gptkb:metaheuristic
stochastic algorithm
global optimization method
|
|
gptkbp:criteria
|
elitism
rank selection
roulette wheel selection
tournament selection
|
|
gptkbp:crossoverType
|
single-point crossover
two-point crossover
uniform crossover
|
|
gptkbp:describedBy
|
gptkb:Adaptation_in_Natural_and_Artificial_Systems
|
|
gptkbp:field
|
gptkb:artificial_intelligence
gptkb:evolutionary_computation
computer science
operations research
|
|
gptkbp:inspiredBy
|
gptkb:natural_selection
genetics
|
|
gptkbp:introduced
|
gptkb:John_Holland
|
|
gptkbp:introducedIn
|
1975
|
|
gptkbp:limitation
|
computational cost
parameter tuning
premature convergence
|
|
gptkbp:mutationAssociatedWith
|
bit flip mutation
scramble mutation
swap mutation
|
|
gptkbp:openSource
|
gptkb:DEAP
gptkb:GAUL
gptkb:PyGAD
gptkb:ECJ
JGAP
|
|
gptkbp:relatedTo
|
gptkb:simulated_annealing
gptkb:particle_swarm_optimization
genetic programming
differential evolution
evolutionary algorithm
|
|
gptkbp:represents
|
gptkb:chromosomal_band
gptkb:gene
bit string
real-valued vector
|
|
gptkbp:step
|
gptkb:mutant
gptkb:examination
crossover
selection
replacement
initialization
termination
|
|
gptkbp:uses
|
gptkb:mutant
crossover
selection
fitness function
population of candidate solutions
|
|
gptkbp:bfsParent
|
gptkb:Simulated_annealing
|
|
gptkbp:bfsLayer
|
7
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Genetic algorithm
|