Removing the Genetics from the Standard Genetic Algorithm

We present an abstraction of the genetic algorithm (GA), termed population-based incremental learning (PBIL), that explicitly maintains the statistics contained in a GA’s population, but which abstracts away the crossover operator and redefines the role of the population. This results in PBIL being simpler, both computationally and theoretically, than the GA. Empirical results reported elsewhere show that PBIL is faster and more effective than the GA on a large set of commonly used benchmark problems. Here we present results on a problem custom designed to benefit both from the GA’s crossover operator and from its use of a population. The results show that PBIL performs as well as, or better than, GAs carefully tuned to do well on this problem. This suggests that even on problems custom designed for GAs, much of the power of the GA may derive from the statistics maintained implicitly in its population, and not from the population itself nor from the crossover operator. Removing the Genetics from the Standard Genetic Algorithm Shumeet Baluja & Rich Caruana May 22, 1995 CMU-CS-95-141 Shumeet Baluja is supported by a National Science Foundation Graduate Fellowship. This research was also partially sponsored by the Wright Laboratory, Aeronautical Systems Center and the Advanced Research Projects Agency under grant F33615-93-1-1330. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, ARPA, or the U.S. Government.