Toward an Interactive Method for DMEA-II and Application to the Spam-Email Detection System

Abstract

Multi-Objective Evolutionary Algorithms (MOEAs) have shown a great potential in dealing with many real-world optimization problems. There has been a popular trend in getting suitable solutions and increasing the convergence of MOEAs by consideration of Decision Makers (DMs) during the optimization process (in other words interacting with DM). Activities of DM includes checking, analyzing the results and giving the preference. In this paper, we propose an interactive method for DMEA-II and apply it to a spam-email detection system. In DMEA-II, an explicit niching operator is used with a set of rays which divides the space evenly for the selection of non-dominated solutions to ï¬ll the solution archive and the population of the next generation. We found that, with DMEA-II solutions will effectively converge to the Pareto optimal set under the guidance of the ray system. By this reason, we propose an interactive method using a set of DM-based rays. This set is generated from reference points that are given by DMs and will replace current original rays in objective space. By using this DM-based rays, the next generation will be guided toward the DM’s preferred region. We carried out a case study on several popular test problems and obtained good results. We apply the proposed method for a real application in a spam-email detection system. With this system, a set of feasible trade-off solutions will be offered for choosing scores and thresholds of the ï¬lter rules.