Then, each program is measured in terms of how well it can perform in a given environment. Based on this test called the fitness measure, the fit programs are selected for the next generation of reproduction. This process is continued until the best solution is determined. (Koza, 1992).
The advantages of genetic programming is that it is an evolving process based on the tested process of natural selection and evolution. This also uses parallel processing and so it can produce more accurate results within a short period of time. Due to these advantages, it is used in many real-world applications.
It plays a profound role in data mining and virtual reality, in every field ranging from finance to gaming. Specialized computer programs can retrieve data from large databases with a lot of precision and speed. These programs can also be used to identify relationships among this data and express them in a way that is best suited for the user. In the field of virtual reality, it can be used to generate the right graphical shapes and sounds to better depict a certain scenario.
Though genetic programming is used in many major applications and fields of study, it also comes with some criticism. The main criticism is that it is very difficult to implement a solution that will solve search as well as optimization problems. Also, genetic programs can only solve problems that have a broad outline. It is not possible to get results when the user starts from zero.
Evolutionary Programming
Evolutionary programming is another optimization technique that is a slight variant of the genetic algorithms. Instead of focusing on the genetic factors of evolution, the evolutionary programming technique lays more emphasis on the behavioral relationship between the parent and offspring. The process is very similar to the other techniques. A random sample is selected and out of this each solution is reproduced to a new population. The fitness of each solution is computed and a tournament selection process is followed to pick the fittest or the best solution. Under this solution, an individual or parent is chosen based on how many other individuals it was able to beat during the tournament.
A notable feature of evolutionary programming...
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