Genetic Algorithms
this paper are excerpted with permission WHICH WE OBTAINED FOR CRC ARTICLE BUT STILL NEED TO GET FOR COMPUTING SURVEYS from S. Forrest, Science 261:872878, Aug. 13, 1993 (Copyright 1993 American Association for the Advancement of Science) and from [4, 2, 3] NOTE: we also need to acknowledge that this comes from the longer CRC article. erated for many generations, the overall fitness of the population generally improves, and the individuals in the population represent improved "solutions" to whatever problem was posed in the fitness function. Figure 1 shows an example of a genetic algorithm being used to optimize a simple function. There are many ways of implementing this idea, the most prevalent being the strategy introduced by Holland [6, 5, 2] and illustrated in Figure 1. In recent years, "genetic algorithms " have taken many forms, and in some cases bear little resemblance to Holland's original formulation. Researchers have experimented with different types of representations, crossover and mutation operators, special-purpose operators, and different approaches to reproduction and selection. However, all of these methods have a family resemblance in that they take some inspiration from biological evolution and from Holland's original genetic algorithm. Books that describe the theory and practice of genetic algorithms in greater detail include [6, 5, 1, 8, 7, 9]. The genetic algorithm is interesting from a computational standpoint, at least in part, because of the claims that have been made about its effectiveness as a biased sampling algorithm. The classical argument about genetic algorithm performance has three components: