Grammatical evolution

Part of a series on the
Evolutionary algorithm
Chromosome Fitness function Genetic operator Crossover Mutation Selection Population model
Genetic algorithm (GA)
Chromosome Clonal selection algorithm Fly algorithm Genetic fuzzy systems Genetic memory Schema Promoter based GA
Genetic programming (GP)
Cartesian GP Linear GP Gene expression programming Grammatical evolution Multi expression programming
Differential evolution
Evolution strategy
Evolutionary programming
Related topics
Cellular EA Cultural algorithm Effective fitness Evolutionary computation Gaussian adaptation Grammar induction Evolutionary multimodal optimization Memetic algorithm Neuroevolution
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Grammatical evolution (GE) is a genetic programming (GP) technique (or approach) from evolutionary computation pioneered by Conor Ryan, JJ Collins and Michael O'Neill in 1998^[1] at the BDS Group in the University of Limerick.

As in any other GP approach, the objective is to find an executable program, program fragment, or function, which will achieve a good fitness value for a given objective function. In most published work on GP, a LISP-style tree-structured expression is directly manipulated, whereas GE applies genetic operators to an integer string, subsequently mapped to a program (or similar) through the use of a grammar, which is typically expressed in Backus–Naur form. One of the benefits of GE is that this mapping simplifies the application of search to different programming languages and other structures.