|
gptkbp:instanceOf
|
gptkb:algorithm
optimization technique
|
|
gptkbp:advantage
|
robustness
parallelism
computational cost
parameter sensitivity
premature convergence
global search capability
|
|
gptkbp:component
|
census
chromosomal band
fitness function
crossover operator
mutation operator
replacement operator
selection operator
termination condition
|
|
gptkbp:describedBy
|
gptkb:Adaptation_in_Natural_and_Artificial_Systems
|
|
gptkbp:field
|
gptkb:artificial_intelligence
gptkb:evolutionary_computation
computer science
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Genetic Algorithms
|
|
gptkbp:implementedIn
|
gptkb:Java
gptkb:Python
gptkb:C++
gptkb:MATLAB
|
|
gptkbp:inspiredBy
|
gptkb:natural_selection
genetics
|
|
gptkbp:introduced
|
gptkb:John_Holland
|
|
gptkbp:introducedIn
|
1975
|
|
gptkbp:openSource
|
gptkb:DEAP
gptkb:GAUL
gptkb:PyGAD
gptkb:ECJ
|
|
gptkbp:relatedTo
|
gptkb:simulated_annealing
gptkb:particle_swarm_optimization
genetic programming
evolutionary algorithms
differential evolution
swarm intelligence
|
|
gptkbp:usedFor
|
gptkb:machine_learning
robotics
optimization
game playing
scheduling
engineering design
function optimization
search problems
|
|
gptkbp:uses
|
mutant
crossover
selection
population of candidate solutions
|
|
gptkbp:bfsParent
|
gptkb:mathematical_optimization
|
|
gptkbp:bfsLayer
|
5
|