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Genetic programming often uses tree-based internal data structures to represent the computer programs for adaptation instead of the list structures typical of genetic algorithms. There are many variants of Genetic Programming, including Cartesian genetic programming , Gene expression programming , [ 62 ] grammatical evolution , Linear genetic ...
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting ...
In November 2009 the program was made available to download as freeware. [4] Lipson described the tool's benefit as dealing with fields that are overwhelmed with data but lack theory to explain it. [5] In the October 2011 edition of "Physical Biology", Lipson described a yeast experiment that predicted seven known equations. [6]
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. [ 1 ] [ 2 ] Evolutionary programming differs from evolution strategy ES( μ + λ {\displaystyle \mu +\lambda } ) in one detail. [ 1 ]
Three branches emerged in different places to attain this goal: evolution strategies, evolutionary programming, and genetic algorithms. A fourth branch, genetic programming, eventually emerged in the early 1990s. These approaches differ in the method of selection, the permitted mutations, and the representation of genetic data.
Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection according to a predefined fitness measure , mutation and crossover .
Gene expression programming [5] belongs to the family of evolutionary algorithms and is closely related to genetic algorithms and genetic programming. From genetic algorithms it inherited the linear chromosomes of fixed length; and from genetic programming it inherited the expressive parse trees of varied sizes and shapes.
Tree representations in a chromosome are used by genetic programming, an EA type for generating computer programs or circuits. [10] The trees correspond to the syntax trees generated by a compiler as internal representation when translating a computer program. The adjacent figure shows the syntax tree of a mathematical expression as an example.