526 research outputs found
Extremal Optimization: Methods derived from Co-Evolution
We describe a general-purpose method for finding high-quality solutions to
hard optimization problems, inspired by self-organized critical models of
co-evolution such as the Bak-Sneppen model. The method, called Extremal
Optimization, successively eliminates extremely undesirable components of
sub-optimal solutions, rather than ``breeding'' better components. In contrast
to Genetic Algorithms which operate on an entire ``gene-pool'' of possible
solutions, Extremal Optimization improves on a single candidate solution by
treating each of its components as species co-evolving according to Darwinian
principles. Unlike Simulated Annealing, its non-equilibrium approach effects an
algorithm requiring few parameters to tune. With only one adjustable parameter,
its performance proves competitive with, and often superior to, more elaborate
stochastic optimization procedures. We demonstrate it here on two classic hard
optimization problems: graph partitioning and the traveling salesman problem.Comment: 8 pages, Latex, 5 ps-figures included. To appear in ``GECCO-99:
Proceedings of the Genetic and Evolutionary Computation Conference,'' (Morgan
Kaufmann, San Francisco, 1999
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
We propose a method to improve image clustering using sparse text and the wisdom of the crowds. In particular, we present a method to fuse two different kinds of document features, image and text features, and use a common dictionary or “wisdom of the crowds” as the connection between the two different kinds of documents. With the proposed fusion matrix, we use topic modeling via non-negative matrix factorization to cluster documents
- …