Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem.Starting with a primordial ooze of thousands of randomly created computer programs, a population of programs is progressively evolved over a series of generations. The evolutionary search uses the Darwinian principle of survival of the fittest and is patterned after naturally occurring operations, including crossover (sexual recombination), mutation, gene duplication, gene deletion, and certain developmental processes by which embryos grow into fully developed organisms. There are now 36 instances where genetic programming has automatically produced a computer program that is competitive with human performance.
In this section we present genetic programming, being the fourth member of the evolutionary algorithm family. Besides the particular representation (using trees as chromosomes) it differs from other EA strands in its application area. While the EAs are typically applied to optimization problems, GP could be rather positioned in machine learning. In terms of nature of this deferent problem types, most other EAs are used for finding some input realizing maximum payoff, whereas GP is used to seek models with maximum fit. Clearly, once maximization is introduced, modelling problems can be seen as special cases of optimization. This, in fact, is the basis of using evolution for such tasks: models are treated as individuals, their fitness being the model quality to be maximized.
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