Genetic and Evolutionary Algorithms
There are many variations of genetic algorithms (GA). Here, wedescribe a simple scheme to introduce some of the key terms in genetic and evolutionary algorithms. See the main entry on Evolutionary Algorithms for references to specific methods.
In genetic learning, we assume that there is a population of individuals, each of which represents a candidate problem solver for a given task. GAs can be thought of as a family of general purpose search methods that are capable of solving a broad range of problems from optimization and scheduling to robot control. Like evolution, genetic algorithms test each individual from the population and only the fittest survive to reproduce for the next generation. The algorithm creates new generations until at least one individual is found that can solve the problem adequately.
Each problem solver is a chromosome. A position, or set of positions in a chromosome is called a gene. The possible values (from a fixed set of symbols) of a gene are...