Supporting implicit learning via the visualisation of COGA multi-objective data

The paper speculates upon the development of human-centric evolutionary conceptual design systems that support implicit learning through the succinct visual presentation of data relating to both variable and objective space. Various perspectives of multi-objective design information support a constantly improving understanding of both subjective and quantitative relationships between variables and objectives. This information emerges from cluster-oriented genetic algorithm (COGA) output and is further defined by appropriate data mining, processing and visualization techniques. The intention is to support implicit learning and reduce complexity through the presentation of differing perspectives relating to solution / objective interaction and dependencies. It is proposed that the developing systems could support intuitional understanding of the problem domain. Further proposed agent-based support and interactive elements for the various processes are also introduced.