Enhancing the performance of MOEAs: an experimental presentation of a new fitness guided mutation operator.
Konstantinos LiagkourasKostas S. MetaxiotisPublished in: J. Exp. Theor. Artif. Intell. (2017)
Keyphrases
- mutation operator
- evolutionary algorithm
- multiobjective evolutionary algorithms
- multi objective optimization
- crossover and mutation
- nsga ii
- genetic algorithm
- fitness function
- multi objective
- differential evolution
- multi objective evolutionary algorithms
- crossover operator
- premature convergence
- population diversity
- optimization problems
- evolutionary programming
- evolutionary process
- evolutionary computation
- function optimization
- genetic operators
- genetic programming
- search direction
- simulated annealing
- test problems
- multiobjective evolutionary algorithm
- multiobjective optimization
- crossover and mutation operators
- initial population
- convergence rate
- selection operator
- convergence speed
- optimization algorithm
- evolution strategy
- optimization method
- evolutionary strategy
- optimal solution
- upper bound
- metaheuristic
- branch and bound algorithm
- benchmark problems
- step size