Investigation of Kriging-based SAEAs' metamodel samples for computationally expensive optimization problems.
Mônica A. C. ValadãoAndré MaravilhaLucas S. BatistaPublished in: Evol. Intell. (2024)
Keyphrases
- computationally expensive
- metamodel
- optimization problems
- black box
- modeling language
- life cycle
- evolutionary algorithm
- development process
- uml profile
- candidate solutions
- reference model
- data model
- model driven
- cost function
- computationally efficient
- metaheuristic
- software systems
- objective function
- transformation rules
- platform independent
- design patterns
- model driven architecture
- data sets
- test cases
- simulated annealing
- training set
- development processes
- data driven
- reinforcement learning
- decision making
- machine learning