Illinois Genetic Algorithms Laboratory

Llorà, X., Matsumura, N., Goldberg, D. E., Ohsawa, Y., Ohnishi, K., Gonzales, A. (2004)
TR No.: 2004026 | Download PDF | Download PS

Abstract:
A successful process of chance discovery using the visual maps proposed
by KeyGraphs requires the usage of graphs with an appropriate degree of
complexity. Complex KeyGraphs often prevent users from discovering
chances because of the difficulties of interpretation. On the other hand,
overly simplistic KeyGraphs seldom includes a chance because of the
sparseness of information. In a useful KeyGraphs the concept clusters
should be easy to find, the clusters should be easy to understand, and
the relations among them should be easy to comprehend and help in the
process of chance identification. This paper systematize the process of
KeyGraph
exploration by means of evolutionary computation, as well as structural
graph properties—such as small-world topologies. The proposed techniques
are successfully applied to create useful KeyGraphs for chance discovery
from several documents.