Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives.
Alexandru-Ciprian ZavoianuGerd BramerdorferEdwin LughoferSiegfried SilberWolfgang AmrheinErich-Peter KlementPublished in: Eng. Appl. Artif. Intell. (2013)
Keyphrases
- multi objective evolutionary algorithms
- artificial neural networks
- multi objective
- multi objective optimization
- multi objective optimization problems
- neural network
- fitness function
- test problems
- genetic algorithm
- genetic algorithm ga
- multi objective problems
- grammar guided genetic programming
- differential evolution
- multi criteria
- evolutionary algorithm
- nsga ii
- computational intelligence
- evolutionary computation
- neural network model
- multiobjective optimization
- bi objective
- multiple objectives
- optimization algorithm
- pareto optimal solutions
- genetic programming