| |
Difino
| • | Genetic programming (GP) is an automated methodology inspired by biological evolution to find computer programs that best perform a user-defined task. It is therefore a particular machine learning technique that uses an evolutionary algorithm to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task. The first experiments with GP were reported by Smith (1980) and Cramer (1985), as described in the famous book Genetic Programming: On the Programming of Computers by Means of Natural Selection by John Koza (1992).
Computer programs in GP can be written in a variety of programming languages. In the early (and traditional) implementations of GP, program instructions and data values were organized in tree-structures, thus favoring the use of languages that naturally embody such a structure (an important example pioneered by Koza is Lisp). Other forms of GP have been suggested and successfully implemented, such as the simpler linear representation which suits the more traditional imperative languages (1998). The commercial GP software Discipulus, for example, uses linear genetic programming combined with machine code language to achieve better performance. Differently, the MicroGP uses an internal representation similar to linear genetic programming to generate programs that fully exploit the syntax of a given assembly language.
GP is very computationally intensive and so in the 1990s it was mainly used to solve relatively simple problems. However, more recently, thanks to various improvements in GP technology and to the well known exponential growth in CPU power, GP has started delivering a number of outstanding results. At the time of writing, nearly 40 human-competitive results have been gathered, in areas such as quantum comput
Source: [wikipedia: genetic programming]
|
alebrije.info
:
alebrijes
:
sites
:
advertising
:
link to us
:
contact
|
|