genetic programming - ορισμός. Τι είναι το genetic programming
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Τι (ποιος) είναι genetic programming - ορισμός

TECHNIQUE WHEREBY COMPUTER PROGRAMS ARE ENCODED AS A SET OF GENES
Genetic Programming; Meta-genetic programming; Applications of genetic programming
  • Animation of creating genetic programing child by mutating parent removing subtree and replacing with random code

genetic programming         
<programming> (GP) A programming technique which extends the genetic algorithm to the domain of whole computer programs. In GP, populations of programs are genetically bred to solve problems. Genetic programming can solve problems of system identification, classification, control, robotics, optimisation, game playing, and pattern recognition. Starting with a primordial ooze of hundreds or thousands of randomly created programs composed of functions and terminals appropriate to the problem, the population is progressively evolved over a series of generations by applying the operations of Darwinian fitness proportionate reproduction and crossover (sexual recombination). (1995-03-31)
Genetic programming         
In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.
Cartesian genetic programming         
FORM OF GENETIC PROGRAMMING, WHICH USES A GRAPH REPRESENTATION TO ENCODE COMPUTER PROGRAMS
Draft:Cartesian Genetic Programming; Cartesian Genetic Programming
Cartesian genetic programming is a form of genetic programming that uses a graph representation to encode computer programs. It grew from a method of evolving digital circuits developed by Julian F.

Βικιπαίδεια

Genetic programming

In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.

The operations are: selection of the fittest programs for reproduction (crossover) and mutation according to a predefined fitness measure, usually proficiency at the desired task. The crossover operation involves swapping random parts of selected pairs (parents) to produce new and different offspring that become part of the new generation of programs. Mutation involves substitution of some random part of a program with some other random part of a program. Some programs not selected for reproduction are copied from the current generation to the new generation. Then the selection and other operations are recursively applied to the new generation of programs.

Typically, members of each new generation are on average more fit than the members of the previous generation, and the best-of-generation program is often better than the best-of-generation programs from previous generations. Termination of the evolution usually occurs when some individual program reaches a predefined proficiency or fitness level.

It may and often does happen that a particular run of the algorithm results in premature convergence to some local maximum which is not a globally optimal or even good solution. Multiple runs (dozens to hundreds) are usually necessary to produce a very good result. It may also be necessary to have a large starting population size and variability of the individuals to avoid pathologies.