What is Genetic Programming?
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One of the central challenges of computer science is to get a computer to do what needs to be done, without telling it how to do it. Genetic programming addresses this challenge by providing a method for automatically creating a working computer program from a high-level problem statement of the problem. Genetic programming achieves this goal of automatic programming (also sometimes called program synthesis or program induction) by genetically breeding a population of computer programs using the principles of Darwinian natural selection and biologically inspired operations. The operations include reproduction, crossover (sexual recombination), mutation, and architecture-altering operations patterned after gene duplication and gene deletion in nature.
Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of naturally occurring genetic operations. The genetic operations include crossover (sexual recombination), mutation, reproduction, gene duplication, and gene deletion.
Preparatory Steps of Genetic Programming
The human user communicates the high-level statement of the problem to the genetic programming system by performing certain well-defined preparatory steps.
The five major preparatory steps for the basic version of genetic programming require the human user to specify
Executional Steps of Genetic Programming
Genetic programming typically starts with a population of randomly generated computer programs composed of the available programmatic ingredients. Genetic programming iteratively transforms a population of computer programs into a new generation of the population by applying analogs of naturally occurring genetic operations. These operations are applied to individual(s) selected from the population. The individuals are probabilistically selected to participate in the genetic operations based on their fitness (as measured by the fitness measure provided by the human user in the third preparatory step). The iterative transformation of the population is executed inside the main generational loop of the run of genetic programming.
The executional steps of genetic programming (that is, the flowchart of genetic programming) are as follows:
(a) Execute each program in the population and ascertain its fitness (explicitly or implicitly) using the problem’s fitness measure.
3. After the termination criterion is satisfied, the single best program in(i) Reproduction: Copy the selected individual program to the new population.
the population produced during the run (the best-so-far individual) is harvested and designated as the result of the run. If the run is successful, the result may be a solution (or approximate solution) to the problem.
Genetic Programming Flow Chart
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