Who's better? PESA or NSGA II?
Laura Silvia Dioşan, Mihai Oltean · Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007) · 2007
According to the No Free Lunch (NFL) theorems all black-box algorithms perform equally well when compared over the entire set of optimization problems. An important problem related to NFL is finding a test problem for which a given algorithm is better than another given algorithm. In this paper we propose an evolutionary approach for solv- ing this problem: we will evolve multi-objective test func- tions for which a given algorithm A is better than another given algorithm B. The evolved functions are represented as binary strings. Several numerical experiments involv- ing PESA and NSGA II are performed. The results show the effectiveness of the proposed approach. Several multi- objective problems for which PESA performs better than NSGA II and several multi-objective test problems for which NSGA II performs better than PESA have been evolved.