A Unified Automated Innovization Framework Using Threshold-based Clustering

Automated Innovization procedure aims to extract hidden, non-intuitive, closed-form relationships from a design task without human intervention. Existing procedures involve the application of an Evolutionary Multi-objective Optimization (EMO) Algorithm in two phases. The first phase of EMO algorithm leads to a set of Pareto-optimal (PO) solutions, while the second phase helps identify the implicit relationships. The latter involves clustering which in turn enables the evaluation of innovization-driven objective function. The existing procedures for Automated Innovization differ in their clustering technique and objective formulation. Unlike any existing study, this paper proposes a Unified Automated Innovization (UAI) framework which can deal with both continuous and discrete variable problems, and identify the inherent single- or multiple-cluster rules, as the case may be. The scope and efficacy of the proposed UAI, demonstrated through some benchmark design problems, is rooted in the novel contributions made in the clustering technique, and innovization-driven objective function formulation(s).