Choosing Mutation and Crossover Ratios for Genetic Algorithms - A Review with a New Dynamic Approach.
Ahmad B. A. HassanatKhalid AlmohammadiEsra'a AlkafaweenEman AbunawasAwni Mansoar HammouriV. B. Surya PrasathPublished in: Inf. (2019)
Keyphrases
- genetic algorithm
- evolutionary algorithm
- differential evolution
- mutation operator
- crossover operator
- fitness function
- genetic algorithm ga
- genetic programming
- genetic operators
- multi objective
- simulated annealing
- population size
- crossover and mutation
- artificial neural networks
- neural network
- candidate solutions
- evolutionary computation
- optimization method
- function optimization problems
- genetic search
- function optimization
- mutation rate
- case study
- selection strategy
- fuzzy logic
- particle swarm optimization
- evolution strategy
- optimization problems
- tabu search
- dynamic environments
- global search
- optimization algorithm
- convergence speed
- traveling salesman problem
- metaheuristic