Genetic Algorithm

Genetic programming (GP) is one of the most useful, general-purpose problem solving techniques available to developers. It has been used to solve a wide range of problems, such as symbolic regression, data mining, optimization, and emergent behavior in biological communities.GP is one instance of the class of techniques called evolutionary algorithms, which are based on insights from the study of natural selection and evolution. Living things are extraordinarily complex, far more so than even the most advanced systems designed by humans. Evolutionary algorithms solve problems not by explicit design and analysis, but by a process akin to natural selection.An evolutionary algorithm solves a problem by first generating a large number of random problem solvers (programs). Each problem solver is executed and rated according to a fitness metric defined by the developer. In the same way that evolution in nature results from natural selection, an evolutionary algorithm selects the best problem solvers in each generation and breeds them.Genetic programming and genetic algorithms are two different evolutionary algorithms. Genetic algorithms involve encoded strings that represent particular problem solutions. These encoded strings are run through a simulator and the best strings are mixed to form a new generation. Genetic programming, the subject of this article, follows a different approach. Instead of encoding a representation of a solution, GP breeds executable computer programs