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Everything about Gene expression programming

Evolutionary algorithm

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. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Gene expression programmingA 5-minute orientation
  2. Skim a recent paper: Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression ProgrammingInfluential work since 2022
  3. Pick up a classic book: Analyzing High-Dimensional Gene Expression and DNA Methylation Data with RThe most-reprinted book on the subject
  4. Read the landmark paper: Programming gene expression in developing epidermisCited 432 times
  5. Go sideways: Genetic programmingA closely related topic
  6. Go sideways: Genetic algorithmA closely related topic

📖 Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Background
  2. Encoding: the genotype
  3. Expression trees: the phenotype
  4. K-expressions and genes
  5. Multigenic chromosomes
  6. Cells and code reuse
  7. Other levels of complexity
  8. The basic gene expression algorithm
  9. The GEP-RNC algorithm
  10. Neural networks
  11. Decision trees
  12. Criticism

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics