  The acquisition of competence is a key element in the ability to assert
oneself in the complex and rapidly changing modern worlds of work. This paper
examines the evolution of competence, i.e. the role of competences in an
evolutionary problem-solving process, and the role of flexibility as a
meta-competence from the perspective of the general concept of
Geometrically-Oriented Evolution THEory (G_O_E_THE).
  We use evolutionary search agents as abstract models of real individuals and
groups. In a first approach the agents search for better competence profiles.
In a second approach they use competences to solve problems. A search agent
operates in an abstract value landscape, a fitness landscape. The crucial
difference between the evolutionary search agents and other agent models is the
fact that interaction patterns of the new agents incorporate evolutionary
search strategies.
  A special simulation tool called EVOLINO allows to simulate the hill-climbing
process of search agents in complex landscapes.
