Low-Dimensional Euclidean Embedding for Visualization of Search Spaces in Combinatorial Optimization.
Krzysztof MichalakPublished in: IEEE Trans. Evol. Comput. (2019)
Keyphrases
- combinatorial optimization
- low dimensional
- multidimensional scaling
- euclidean space
- multi dimensional scaling
- branch and bound
- search space
- metaheuristic
- embedding space
- pairwise distances
- nonlinear dimensionality reduction
- high dimensional
- euclidean distance
- vector space
- combinatorial search
- combinatorial optimization problems
- traveling salesman problem
- graph embedding
- dimensionality reduction
- branch and bound algorithm
- optimization problems
- simulated annealing
- nonlinear manifold learning
- data points
- high dimensional data
- manifold learning
- principal component analysis
- latent space
- combinatorial problems
- laplacian eigenmaps
- geodesic distance
- vehicle routing problem
- feature space
- mathematical programming
- quadratic assignment problem
- hard combinatorial optimization problems
- evolutionary algorithm
- solution space
- data analysis
- input space
- distance measure
- shape analysis
- metric space
- low dimensional manifolds
- ant colony optimization
- lower bound
- hamming space
- feature selection