3 May 1995, 12:00, WeH 1327 Removing the Genetics from the Standard Genetic Algorithm Shumeet Baluja I will 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 show that PBIL is faster and more effective than the GA on a set of 26 commonly used benchmark problems (PBIL does better than the GAs used for comparison on 23 out of the 26 problems). In this talk, however, I will just concentrate on one of the problems attempted. This problem is 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 very 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 two most distinguishing features of genetic algorithms: the population and the crossover operator. Hopefully, there will be quite a bit of time for discussion; questions and comments are especially welcome.