Selecting mutation operators with a multiobjective approach.
Adam S. BanziTiago NobreGabriel B. PinheiroJoão Carlos G. ÁriasAurora T. R. PozoSilvia Regina VergilioPublished in: Expert Syst. Appl. (2012)
Keyphrases
- multi objective
- mutation operator
- evolutionary algorithm
- nsga ii
- genetic algorithm
- differential evolution
- optimization algorithm
- crossover operator
- multi objective optimization
- multiobjective optimization
- premature convergence
- genetic operators
- particle swarm optimization
- pareto optimal
- optimization problems
- evolutionary computation
- bi objective
- multiobjective evolutionary algorithm
- crossover and mutation
- fitness function
- genetic programming
- simulated annealing
- multiobjective genetic algorithm
- multiple objectives
- uniform design
- evolution strategy
- multi objective evolutionary algorithms
- artificial bee colony
- conflicting objectives
- test problems
- multiobjective evolutionary algorithms
- objective function
- genetic search
- initial population
- crossover and mutation operators
- artificial neural networks
- pareto optimal solutions
- selection operator