|
gptkbp:instance_of
|
gptkb:Artificial_Intelligence
|
|
gptkbp:analyzes
|
Fitness Landscapes
Evolutionary Trees
|
|
gptkbp:based_on
|
gptkb:Darwinian_Evolution
|
|
gptkbp:can_be_combined_with
|
gptkb:neural_networks
Fuzzy Logic
Swarm Intelligence
|
|
gptkbp:can_be_used_for
|
Data Mining
Image Processing
Feature Selection
Parameter Optimization
|
|
gptkbp:can_converge_to
|
Local Optima
Global Optima
|
|
gptkbp:composed_of
|
gptkb:Census-designated_place
Chromosomes
Genes
|
|
gptkbp:developed_by
|
gptkb:John_William_Holland
|
|
gptkbp:evaluates
|
Population Fitness
|
|
gptkbp:first_introduced
|
gptkb:1975
|
|
gptkbp:has_applications_in
|
gptkb:Telecommunications
gptkb:Biology
Engineering
Finance
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Genetic Algorithms
|
|
gptkbp:inspired_by
|
Natural Selection
|
|
gptkbp:involves
|
gptkb:cross
gptkb:Genetics
Selection
|
|
gptkbp:is_applied_in
|
gptkb:robotics
Game Development
Scheduling Problems
Traveling Salesman Problem
|
|
gptkbp:is_considered_as
|
Search Algorithm
Metaheuristic
Adaptive Algorithm
|
|
gptkbp:is_evaluated_by
|
Performance Metrics
Benchmark Problems
|
|
gptkbp:is_implemented_in
|
gptkb:MATLAB
gptkb:Java
gptkb:C++
gptkb:Python
|
|
gptkbp:is_influenced_by
|
gptkb:strategy
Complex Systems
Chaos Theory
|
|
gptkbp:is_optimized_for
|
Complex Problems
|
|
gptkbp:is_popular_in
|
gptkb:Industry
gptkb:scientific_community
|
|
gptkbp:is_related_to
|
gptkb:Artificial_Intelligence
gptkb:Genetic_Programming
Evolutionary Computation
|
|
gptkbp:requires
|
Parameter Tuning
Randomness
|
|
gptkbp:runs_through
|
gptkb:True
|
|
gptkbp:suitable_for
|
Small Data Sets
Highly Constrained Problems
|
|
gptkbp:used_in
|
gptkb:Artificial_Intelligence
gptkb:machine_learning
Optimization
|
|
gptkbp:uses
|
Fitness Function
|
|
gptkbp:bfsParent
|
gptkb:John_William_Holland
|
|
gptkbp:bfsLayer
|
4
|