Constrained optimization by the evolutionary algorithm with lower dimensional crossover and gradient-based mutation.
Qing ZhangSanyou ZengRui WangHui ShiGuang ChenLixin DingLishan KangPublished in: IEEE Congress on Evolutionary Computation (2008)
Keyphrases
- lower dimensional
- evolutionary algorithm
- constrained optimization
- mutation operator
- crossover operator
- differential evolution
- constrained optimization problems
- multi objective
- dimensionality reduction
- high dimensional
- higher dimensional
- original data
- fitness function
- optimization problems
- low dimensional
- genetic algorithm
- principal component analysis
- genetic operators
- objective function
- high dimensional data
- random projections
- genetic programming
- simulated annealing
- mutation operation
- feature space
- penalty function
- multi objective optimization
- optimization method
- function optimization
- evolutionary process
- mutation rate
- premature convergence
- nsga ii
- evolution strategy
- crossover and mutation
- evolutionary strategy
- image processing
- data sets
- population diversity
- initial population
- particle swarm
- genetic algorithm ga
- population size
- pattern recognition
- feature selection
- nearest neighbor
- convergence rate