Survival of the Fittest: Natural Selection with the .NET Framework

摘要

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.