Interactive Evolutionary Multiobjective Optimization of Primer Design with Uncertain Objectives

The choice of primer designs for polymerase chain reaction experiments affects the results. Designing optimal combinations of forward and reverse primers requires solving multiple conflicting objectives simultaneously. Most of the tools for primer design optimize the problem by a priori scalarization or by setting constraints with preset preferences. Therefore, the decision-maker (DM) or domain expert has to re-execute the optimizer with new preferences to find satisfactory solutions. An a priori method is detrimental to decision-making since the DM cannot learn about the problem characteristics, and re-executing the optimizer with new preferences increases the number of function evaluations. In addition, the existing methods rely on a single mathematical model to estimate the melting temperature of primers. In this paper, we formulate a multiobjective optimization problem consisting of three uncertain objectives that use six different models to estimate the melting temperatures of primers. The formulated problem was solved using an interactive multiobjective evolutionary algorithm that enabled the DM to guide the solution process. We also proposed a selection criterion tailored to our problem that could find optimal primer designs according to the DM's preferences. Finally, we demonstrate the proposed interactive approach to find optimal primers for a bacterial 16S DNA sequence.