John Holland, a professor at the University of Michigan at the time, is credited with the invention of genetic algorithms following the publication of his 1975 book Adaptation in Natural and Artificial Systems . In his book Holland formulated the basics of genetic algorithms as models of machine learning that derive their behavior from concepts of biology's theory of evolution. It was one of Holland's students, David Goldberg, who popularized the use of genetic algorithms when he was able to solve a difficult problem involving gas-pipeline transmission for his dissertation in 1989. A genetic algorithm can be thought of as a model for machine learning in which a population of randomly created individuals goes through a simulated process of evolution - a digital survival of the fittest where each individual represents a point in the problem's solution search space. Using correct terminology, an individual is represented by a chromosome, which consists of several genes.
Genes are essentially the parameters of the problem to be solved.
A collection of chromosomes is considered a population and is the fundamental unit on which a genetic algorithm operates. Once the algorithm is set into motion, individuals are selected from a population and combined in a process called crossover to create a set of children. The children are randomly mutated to create a new set of chromosomes to be reinserted into the population. Once enough children chromosomes have been created to replace a population, a generation is said to have passed. Java and Java-based marks are trademarks or registered trademarks of Sun Microsystems, Inc. in the United States and other countries.