Comparison of various encoding schemes for tree in Biobjective optimization scenario

To find MST (Minimum Spanning Trees) in complete graph is a classical problem in operation research having network design as an important application. It is possible to solve MST problem efficiently, but its Biobjective versions are NP hard. In this paper, we present a comparison of two encoding schemes for representing tree in Biobjective optimization scenario. The three different instances of Biobjective MST have been solved by using two different encoding methods in the evolutionary algorithms; Pareto optimal front obtained has been taken into account for comparative study. Our evolutionary computation approach involves Biobjective MST problem using NSGAII (Nondominated Sorting Genetic Algorithm II) approach. We compare edge sets encoding with Prüfer encoding in evolutionary algorithm for Biobjective MST, we find edge sets encoding outperforms the Prüfer in Biobjective optimization scenario.