Chromosomes' Strings On a parallel computer , the individual members of a chromosomal population can test simultaneously, sample, and explore large portions of the search space independently of one another. Crossover Crossover is particularly important to genetic algorithms . More specifically, it exchanges genetic material from two parent chromosomes, allowing beneficial genes from each to be combined in their offspring. In other words, it allows improvements in the solutions to a problem. The combination of reproduction and the information exchange of crossover give genetic algorithms much of their power.
Crossover allows a very large number of attempted solutions to be tested simultaneously.
Selection is a filter that determines by competition which individuals within a population survive to reproduce. Reproduction permits innovation through the alteration of genetic traits and thus provides a route for the evolution of increasingly successful individuals. This refers to programming computers to make decisions in real-life situations for example, some expert systems help doctors diagnose diseases based on symptoms. Neural Networks, represent a class of computer programs that, like genetic algorithms, apply nature's strategies in a wider context. They are systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains.