The paper describes further developments of the interactive evolutionary design concept relating to the emergence of mutually inclusive regions of high performance solutions relating to differing objectives from cluster-oriented genetic algorithm (COGAs) output. These common regions are further defined by the application of clustering algorithms and relevant variable analysis. The multi-objective output of the COGA is then compared to output from a strength Pareto evolutionary algorithm (SPEA-II).