Statements (56)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
optimization technique search heuristic |
| gptkbp:application |
gptkb:machine_learning
robotics optimization problems game playing scheduling engineering design |
| gptkbp:canBe |
control systems
bioinformatics feature selection financial modeling data mining neural network training parameter tuning evolving strategies function optimization |
| gptkbp:field |
gptkb:artificial_intelligence
computer science operations research |
| gptkbp:hasComponent |
gptkb:gene
census generator chromosomal band fitness landscape |
| https://www.w3.org/2000/01/rdf-schema#label |
genetic algorithms
|
| gptkbp:inspiredBy |
gptkb:natural_selection
evolutionary biology |
| gptkbp:introduced |
gptkb:John_Holland
|
| gptkbp:introducedIn |
1975
|
| gptkbp:limitation |
computational cost
no guarantee of global optimum parameter sensitivity premature convergence |
| gptkbp:relatedTo |
gptkb:simulated_annealing
genetic programming evolutionary algorithms differential evolution swarm intelligence |
| gptkbp:step |
examination
mutant crossover selection replacement |
| gptkbp:uses |
mutant
crossover selection fitness function population of candidate solutions |
| gptkbp:bfsParent |
gptkb:6.034_Artificial_Intelligence_(MIT)
gptkb:game_AI gptkb:subsymbolic_AI gptkb:John_Holland gptkb:evolutionary_computation |
| gptkbp:bfsLayer |
5
|