A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems.
Nicola DemoMarco TezzeleGianluigi RozzaPublished in: CoRR (2020)
Keyphrases
- high dimensional
- optimization problems
- genetic algorithm
- supervised learning
- evolutionary algorithm
- metaheuristic
- low dimensional
- high dimensional data
- parameter space
- unsupervised learning
- fitness function
- data points
- training samples
- dimensionality reduction
- cost function
- feature space
- multi objective
- nearest neighbor
- high dimensionality
- tabu search
- semi supervised
- objective function
- data sets
- learning algorithm
- dimension reduction
- sparse data
- multi dimensional
- multi objective optimization
- microarray data
- traveling salesman problem
- lower dimensional
- manifold learning
- machine learning
- training set
- active learning
- sparse coding
- high dimensional feature spaces
- high dimensional feature space
- artificial neural networks
- subspace clustering
- generalization error
- variable selection
- simulated annealing
- genetic algorithm ga
- genetic programming
- particle swarm optimization
- unlabeled data
- similarity search
- gene expression data
- ant colony optimization
- combinatorial optimization
- optimization method