Statements (64)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
mathematical optimization search heuristic |
| gptkbp:application |
gptkb:machine_learning
robotics optimization game playing scheduling engineering design |
| gptkbp:category |
stochastic algorithm
metaheuristic 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 |
| https://www.w3.org/2000/01/rdf-schema#label |
Genetic algorithm
|
| 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:gene
chromosomal band bit string real-valued vector |
| gptkbp:step |
examination
mutant crossover selection replacement initialization termination |
| gptkbp:uses |
mutant
crossover selection fitness function population of candidate solutions |
| gptkbp:bfsParent |
gptkb:Simulated_annealing
gptkb:Metaheuristics |
| gptkbp:bfsLayer |
7
|