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...

Its major advantage is that there is no fixed representation like encoding the solutions as bit strings. Also, since crossover is not used as a genetic operator, minor variations in the offspring does not lead to major variations. This also avoids the problem of premature convergence.
It can be applied to a wide range of fields like transportation, pharmaceutical, military planning, games, control systems, earthquake determination, sensor array localization, integer optimization, optical systems, image resolution, face recognition systems and even to monitor blood pressure during surgery. (McDonnell, Reynolds & Fogel, 1995).

Despite this extensive application, there are also some problems and issues that come with it. The primary issue is the fitness assessment. When a tournament selection is used, it only determines the best of the existing solutions and not necessarily the optimal solution. It also looks at fitness "as a single resource that has to be shared by individuals occupying the same environmental niche." (McDonnell, Reynolds & Fogel, 1995, p.110). These factors make it difficult to determine the optimal solution every time.

Conclusion

In short, these three techniques of evolutionary computation have changed the world in many ways. They are in the nascent stages of development and as they grow, they are expected to make major break-through in our understanding of problems and our ability to find solutions for these problems quickly and efficiently.

References

Yao, Xin. (1999). Evolutionary Computation: Theory and Applications. Publisher: World Scientific.

Back, Thomas. Fogel, David.B, Michaelewicz, Zbigniew. (2000). Evolutionary Computation 1: Basic Algorithms and Operators. Publisher: CRC Press.

Mitchell, Melanie. (1998). An Introduction to Genetic Algorithms. Publisher: MIT Press.

Koza, John. R. (1992). Genetic Programming: On the Programming of Computers by means of natural selection. Publisher: MIT Press.

McDonnell, John.R, Reynolds, Robert.G, Fogel, David.B. (1995). Evolutionary Programming 4. Publisher: MIT Press.