Removing the Genetics from the Standard Genetic Algorithm Shumeet Baluja Rich Caruana School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University Pittsburgh, PA 15213 Pittsburgh, PA 15213
[email protected] [email protected] Abstract from both parents into their respective positions in a mem- ber of the subsequent generation. Due to random factors involved in producing “children” chromosomes, the chil- We present an abstraction of the genetic dren may, or may not, have higher fitness values than their algorithm (GA), termed population-based parents. Nevertheless, because of the selective pressure incremental learning (PBIL), that explicitly applied through a number of generations, the overall trend maintains the statistics contained in a GA’s is towards higher fitness chromosomes. Mutations are population, but which abstracts away the used to help preserve diversity in the population. Muta- crossover operator and redefines the role of tions introduce random changes into the chromosomes. A the population. This results in PBIL being good overview of GAs can be found in [Goldberg, 1989] simpler, both computationally and theoreti- [De Jong, 1975]. cally, than the GA. Empirical results reported elsewhere show that PBIL is faster Although there has recently been some controversy in the and more effective than the GA on a large set GA community as to whether GAs should be used for of commonly used benchmark problems. static function optimization, a large amount of research Here we present results on a problem custom has been, and continues to be, conducted in this direction. designed to benefit both from the GA’s cross- [De Jong, 1992] claims that the GA is not a function opti- over operator and from its use of a popula- mizer, and that typical GAs which are used for function tion.