Gene expression programming

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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
v t e

Gene expression programming (GEP) in computer programming is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism. And like living organisms, the computer programs of GEP are also encoded in simple linear chromosomes of fixed length. Thus, GEP is a genotype–phenotype system, benefiting from a simple genome to keep and transmit the genetic information and a complex phenotype to explore the environment and adapt to it.