GENETIC ALGORITHM applied to programs. GENETIC PROGRAMMING is more expressive than fixed-length character string GA though GAs are likely to be more efficient for some classes of problems. See Q1.5 for more information.
a programming technique that extends the genetics algorithm to the domain of whole computer programs. It purposes is to automatically generate computer programs that optimally solve a particular problem.
In artificial intelligence, this form of programming automatically generates a program from a set of primitive constructs.
A method of applying simulated EVOLUTION on PROGRAMS or program fragments. Modified forms of MUTATION and CROSSOVER are used along with a FITNESS function.
A form of variable length GA that uses directly acting program instructions as the genes.
A subset of genetic algorithms. The members of the populations are the parse trees of computer programs whose fitness is evaluated by running them. The reproduction operators (e.g. crossover) are refined to ensure that the child is syntactically correct (some protection may be given against semantic errors too). This is achieved by acting upon subtrees. Genetic programming is most easily implemented where the computer language is tree structured so there is no need to explicitly evaluated its parse tree. This is one of the reasons why Lisp is often used for genetic programming. This is the common usage of the term genetic programming however it has also been used to refer to the programming of cellular automata and neural networks using a genetic algorithm.
(GP) A method of applying simulated evolution on programs or program fragments.
Genetic programming (GP) is a patentedhttp://www.genetic-programming.com/patents.html automated methodology inspired by biological evolution to find computer programs that perform a user-defined task. Therefore it is a 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 Stephen F.