Low-Dimensional euclidean embedding for visualization of search spaces in combinatorial optimization.
Krzysztof MichalakPublished in: GECCO (Companion) (2019)
Keyphrases
- combinatorial optimization
- low dimensional
- multidimensional scaling
- euclidean space
- branch and bound
- multi dimensional scaling
- embedding space
- search space
- metaheuristic
- nonlinear dimensionality reduction
- pairwise distances
- euclidean distance
- high dimensional
- graph embedding
- vector space
- manifold learning
- combinatorial optimization problems
- combinatorial search
- dimensionality reduction
- traveling salesman problem
- simulated annealing
- branch and bound algorithm
- high dimensional data
- data points
- combinatorial problems
- nonlinear manifold learning
- principal component analysis
- optimization problems
- mathematical programming
- hard combinatorial optimization problems
- latent space
- input space
- geodesic distance
- laplacian eigenmaps
- feature space
- locally linear embedding
- quadratic assignment problem
- vehicle routing problem
- low dimensional manifolds
- tabu search
- geometric structure
- optimal solution
- shape analysis
- linear dimensionality reduction
- particle swarm optimization
- pattern recognition