Evolutionary search agents in complex landscapes - a new model for the role of competence and meta-competence (EVOLINO and other simulation tools)

Abstract

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.