This paper focuses on two machine learning abstractions springing from
ecological models: (i) evolutionary adaptation by local selection, and
(ii) selective query expansion by internalization of environmental
signals.  We first outline a number of experiments pointing to the
feasibility and performance of these methods on a general class of
graph environments.  We then describe how these methods have been
applied to the intelligent retrieval of information distributed across
networked environments.  In particular, the paper discusses a novel
distributed evolutionary algorithm and representation used to
construct populations of adaptive Web agents.  These {\em InfoSpiders}
search on-line for information relevant to the user, by traversing
hyperlinks in an autonomous and intelligent fashion.  They can adapt
to the spatial and temporal regularities of their local context.  Our
results suggest that InfoSpiders could complement current search
engine technology by starting up where search engines stop.  Engines
provide global starting points, based on statistical features of the
search space (words); InfoSpiders can use topological features (links)
to guide their subsequent search on-line.  We show how this approach
can help overcoming some of the limitations of the current state of
the art, dealing with the problems of scalability, personalization,
and localization.