Challenges with verification, repeatability, and meaningful comparison in genetic programming: gibson's magic
Jason M. Daida, Derrick S. Ampy, Michael Ratanasavetavadhana, Hsiaolei Li, Omar A. Chaudhri · 1999
This paper examines some of the reporting and research practices concerning empirical work in genetic programming. We describe several common loopholes and offer three case studies—two in data modeling and one in robotics—that illustrate each. We show that by exploiting these loopholes, one can achieve performance gains of up two orders of magnitude without any substantiative changes to G P. We subsequently offer several recommendations.