genetic programming


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genetic programming

[jə‚ned·ik ′prō‚gram·iŋ]
(computer science)

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).

genetic programming

A type of programming that imitates genetic algorithms, which uses mutation and replication to produce algorithms that represent the "survival of the fittest." While genetic algorithms yield numbers, genetic programs yield ever-improving computer programs. Written in languages such as LISP and Scheme, genetic programming requires the determination of a fitness function, which is a desired output (result). The degree of error in the fitness function determines the quality of the program. For more information, visit www.geneticprogramming.com.
References in periodicals archive ?
These procedures were developed for genetic algorithms by Holland (1975) and extended to genetic programming by Koza (1992).
Another important evolutionary algorithm, and predecessor of genetic programming, was evolutionary programming.
In Proceedings of the First Annual Conference on Genetic Programming (GP-96), eds.
Genotype-phenotype mapping and neutral variation - A case study in genetic programming.
Replica free repository using genetic programming with decision tree" in International Journal of Advanced Engineering Applications, 1(2): 62-66.
Key words: neural network, network encoding, evolutionary computation, genetic programming, topology optimization
The simulated evolution can be driven by genetic algorithms, genetic programming, evolutionary programming, or evolution strategies.
Countless millennia of genetic programming have prepared them to do so.
We feel, however, that the best way to accomplish this task is to use a heterogeneous approach including numerical optimization techniques, expert systems, and genetic programming.
3 Genetic Programming for Exploring Medical Data using Visual Spaces (Julio J.
Sumathi (electrical and electronics engineering, PSG College of Technology, Coimbatore, India) and Surekha (Adhiyamaan College of Engineering, Hosur, Tamil Nadu) present some theoretical concepts and sketch a general framework for computational intelligence paradigms such as artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms, genetic programming, and swarm intelligence.
In this paper, we propose a methodology based on genetic programming to automatically generate data-flow based specifications for hardware designs of combinational digital circuits.

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