Genetic Algorithms and Explicit Search Statistics
Shumeet Baluja · 1996
The genetic algorithm (GA) is a heuristic search procedure based on mechanisms abstracted from population genetics. In a previous paper [Baluja & Caruana, 1995], we showed that much simpler algorithms, such as hillclimbing and Population-Based Incremental Learning (PBIL), perform comparably to GAs on an optimization problem custom designed to benefit from the GA’s operators. This paper extends these results in two directions. First, in a large-scale empirical comparison of problems that have been reported in GA literature, we show that on many problems, simpler algorithms can perform significantly better than GAs. Second, we describe when crossover is useful, and show how it can be incorporated into PBIL. 1 IMPLICIT VS. EXPLICIT SEARCH STATISTICS Although there has recently been controversy in the genetic algorithm (GA) community as to whether GAs should be used for static function optimization, a large amount of research has been, and continues to be, conducted in this direction [De Jong, 1992]. Since much of GA research focuses on optimization (most often in static environments), this study examines