Commonality and Genetic Algorithms

Abstract

this paper was sponsored in part by the Advanced Research Projects Agency and Rome Laboratory, Air Force Material Command, USAF, under grant number F30602-95-1-0018 and the CMU Robotics Institute. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Advanced Research Projects Agency and Rome Laboratory or the U.S. Government. Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Traditional Crossover Operators . . . . . . . . . . . . . . . . . . . . . . 2 3. The Commonality-Based Crossover Framework . . . . . . . . . . . . . . . 2 4. Sequence-based Crossover Operators . . . . . . . . . . . . . . . . . . . . 2 5. Maximum Partial Order . . . . . . . . . . . . . . . . . . . . . . . . . 5 6. Experimental Results for MPO/AI . . . . . . . . . . . . . . . . . . . . . 6